Gob Spontaneous Combustion in a Fully Mechanized Long-wall Top-Coal Caving Face

Geological conditions allow, underground coal mines in China tend to use comprehensively mechanized roof-coal caving technique in an effort to gain a higher degree of mechanization at coal faces as well as higher coal production rates. As a face advances, a large amount of coal will be left behind in its gob area which may experience a self-enhancing process of coal oxidation and heat accumulation, ultimately leading to open fire. Such a self-enhancing coal spontaneous combustion process is a significantly impeding mine safety and productivity. A sound mathematical model is an important step to predict the probability of spontaneous combustion so that measures against coal self-heating can be adopted in time and at comparatively low cost. This paper analyzes main factors in coal spontaneous combustion process and proposes a mathematical model to describe the dynamic process of coal self-heating in the gob. This model has been applied to a coal production face in Datong Coal Region in Shangdong Province to satisfactorily predict the spontaneous combustion probability

MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features (i.e., malware images and malware sentences) from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated sub-space, i.e., discriminative and exclusive spaces, with contrastive sampling and rho-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and contribute an improved version dubbed MAL-100+. Experimental results on the widely used malware dataset Mailing and the proposed MAL-100+ demonstrate the effectiveness of our method.Comment: 14 pages, 7 figure

CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario

We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. In this paper, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. Moreover, we also build a new large-scale malware dataset, named MAL-100, to fill the gap of lacking large open-set malware benchmark dataset. Experimental results on two widely used malware datasets and our MAL-100 demonstrate the effectiveness of our model compared with other representative methods.Comment: 16 pages, 8 figure

ProCC: Progressive Cross-primitive Consistency for Open-World Compositional Zero-Shot Learning

Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions. Existing works either learn the joint compositional state-object embedding or predict simple primitives with separate classifiers. However, the former heavily relies on external word embedding methods, and the latter ignores the interactions of interdependent primitives, respectively. In this paper, we revisit the primitive prediction approach and propose a novel method, termed Progressive Cross-primitive Consistency (ProCC), to mimic the human learning process for OW-CZSL tasks. Specifically, the cross-primitive consistency module explicitly learns to model the interactions of state and object features with the trainable memory units, which efficiently acquires cross-primitive visual attention and avoids cross-primitive feasibility scores. Moreover, considering the partial-supervision setting (pCZSL) as well as the imbalance issue of multiple tasks prediction, we design a progressive training paradigm to enable the primitive classifiers to interact to obtain discriminative information in an easy-to-hard manner. Extensive experiments on three widely used benchmark datasets demonstrate that our method outperforms other representative methods on both OW-CZSL and pCZSL settings by

A new species of the odorous frog genus Odorrana (Amphibia, Anura, Ranidae) from southwestern China

The genus Odorrana is widely distributed in the mountains of East and Southeastern Asia. An increasing number of new species in the genus have been recognized especially in the last decade. Phylogenetic studies of the O. schmackeri species complex with wide distributional range also revealed several cryptic species. Here, we describe a new species in the species complex from Guizhou Province of China. Phylogenetic analyses based on mitochondrial DNA indicated the new species as a monophyly clustered into the Odorrana clade and sister to O. schmackeri, and nuclear DNA also indicated it as an independent lineage separated from its related species. Morphologically, the new species can be distinguished from its congeners based on a combination of the following characters: (1) having smaller body size in males (snout-vent length (SVL) <43.3 mm); (2) head longer than wide; (3) dorsolateral folds absent; (4) tympanum of males large and distinct, tympanum diameter twice as long as width of distal phalanx of finger III; (5) two metacarpal tubercles; (6) relative finger lengths: II < I < IV < III; (7) tibiotarsal articulation reaching to the level between eye to nostril when leg stretched forward; (8) disks on digits with circum-marginal grooves; (9) toes fully webbed to disks; (10) the first subarticular tubercle on fingers weak; (11) having white pectoral spinules, paired subgular vocal sacs located at corners of throat, light yellow nuptial pad on the first finger in males

Research of the Transmission Accuracy Test Method of Precise Reducer used in Robot

There is no mature test methods of transmission accuracy for precise reducers that are usually used in robot. Based on existing standards and the analysis of applications,the test methods of transmission error,hysteresis error,backlash and torsional stiffness is discussed. The feasible measuring and data processing methods are proposed. A test rig is constructed and three contrast tests are completed for verification of the methods

FIGURE 7 in A new species of the genus Microhyla (Amphibia: Anura: Microhylidae) from Guizhou Province, China

FIGURE 7. Advertisement call of the holotype CIBFJS20180425013 of Microhyla fanjingshanensis sp. nov.. A, waveform showing one note. B, sonogram showing one note. C, waveform showing three notes of one advertisement call. D, sonogram showing three notes of one call.Published as part of Li, Shize, Zhang, Meihua, Xu, Ning, Lv, Jingcai, Jiang, Jianping, Liu, Jing, Wei, Gang & Wang, Bin, 2019, A new species of the genus Microhyla (Amphibia: Anura: Microhylidae) from Guizhou Province, China, pp. 551-575 in Zootaxa 4624 (4) on page 569, DOI: 10.11646/zootaxa.4624.4.7, http://zenodo.org/record/326561

FIGURE 4. The holotype specimen CIBFJS20180425013 in A new species of the genus Microhyla (Amphibia: Anura: Microhylidae) from Guizhou Province, China

FIGURE 4. The holotype specimen CIBFJS20180425013 of Microhyla fanjingshanensis sp. nov.. A, dorsal view. B, ventral view. C, ventral view of hand. D, ventral view of foot.Published as part of Li, Shize, Zhang, Meihua, Xu, Ning, Lv, Jingcai, Jiang, Jianping, Liu, Jing, Wei, Gang & Wang, Bin, 2019, A new species of the genus Microhyla (Amphibia: Anura: Microhylidae) from Guizhou Province, China, pp. 551-575 in Zootaxa 4624 (4) on page 557, DOI: 10.11646/zootaxa.4624.4.7, http://zenodo.org/record/326561

FIGURE 6 in A new species of the genus Microhyla (Amphibia: Anura: Microhylidae) from Guizhou Province, China

FIGURE 6. Color variation in Microhyla fanjingshanensis sp. nov.. A, dorsal view of the male specimen CIBFJS20180425002. B, ventral view of the male specimen CIBFJS20180425002. C, dorsolateral view of the male specimen CIBFJS20180425006. D, ventral view of the male specimen CIBFJS20180425006. E, dorsal view of the female specimen CIBFJS20180425011..Published as part of Li, Shize, Zhang, Meihua, Xu, Ning, Lv, Jingcai, Jiang, Jianping, Liu, Jing, Wei, Gang & Wang, Bin, 2019, A new species of the genus Microhyla (Amphibia: Anura: Microhylidae) from Guizhou Province, China, pp. 551-575 in Zootaxa 4624 (4) on page 560, DOI: 10.11646/zootaxa.4624.4.7, http://zenodo.org/record/326561