Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method of Docker Container Forensics Based on API

As one of the main technologies supporting cloud computing virtualization, Docker is featured in its fast and lightweight virtualization which has been adopted by numerous platform-as-a-service (PaaS) systems, but forensics research for Docker has not been paid the corresponding attention yet. Docker exists to store and distribute illegal information as a carrier for initiating attacks like traditional cloud services. The paper explains Docker service principles and structural features, and analyzing the model and method of forensics in related cloud environment, then proposes a Docker container forensics solution based on the Docker API. In this paper, Docker APIs realize the derivation of the Docker container instances, copying and back-up of the container data volume, extraction of the key evidence data, such as container log information, configuration information and image information, thus conducts localized fixed forensics to volatile evidence and data in the Docker service container. Combined with digital signatures and digital encryption technology to achieve the integrity of the original evidence data protection.
VirtAPCA: An Active and Passive Coexistence Attack Model on Xen

Xen through the Dom0 to manage and control other guest domains to enhance the utilization and efficiency of resources, but it also introduces new security risks. This paper presents a kernel-module-based model for active and passive coexistence attacks in the Xen virtualization environment. The active attack mode of VirtAPCA can destroy all network-based services in Dom0, and the passive mode offers the ability to steal guest domain user's privacy information. VirtAPCA has hidden self-protection features that evade system checking and deletion. Experiments show that the model can successfully perform the attack task. The principles behind VirtAPCA allows it to scale to other virtualization environments such as KVM.
Comparative analyses of longissimus muscle miRNAomes reveal microRNAs associated with differential regulation of muscle fiber development between Tongcheng and Yorkshire pigs

Tongcheng (TC) and Yorkshire (YK) are two pig breeds with distinctive muscle morphology. Porcine microRNAome (miRNAome) of the longissimus muscle during five developmental stages (40, 55, 63, 70, and 90 days post coitum (dpc)) was explored by Solexa sequencing in the present study to find miRNAs involved in the different regulation of skeletal muscle development between the two breeds. A total of 320 known porcine miRNAs, 64 miRNAs corresponding to other mammals, and 224 potentially novel miRNAs were identified. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) suggested that the factor “pig breed” affected the miRNA expression profiles to a lesser extent than the factor “developmental stage”. Fifty-seven miRNAs were differentially expressed (DE) between the neighbor developmental stages in TC and 45 such miRNAs were found in YK, 34 in common; there were more down-regulated stage-DE miRNAs than up-regulated. And a total of 23, 30, 12, 6, and 30 breed-DE miRNAs between TC and YK were identified at 40, 55, 63, 70, and 90 dpc, respectively, which were mainly involved in cellular protein modification process, protein transport, and metabolic process. As the only highly expressed breed-DE miRNA found in no less than four developmental stages, and also a stage-DE miRNA found both in TC and YK, miR-499-5p could bind the 3’-UTR of a myofibrillogenesis regulator, destrin/actin depolymerizing factor (DSTN), as validated in dual luciferase reporter assay. The results suggested that miR-499-5p possibly play a noteworthy role in the breed-distinctive porcine muscle fiber development associated with the regulation of DSTN.
Revealing and Quantifying the Three-Dimensional Nano- and Microscale Structures in Self-Assembled Cellulose Microfibrils in Dispersions

Cellulose microfibrils (CMFs) are an important nanoscale building block in many novel biobased functional materials. The spatial nano- and microscale organization of the CMFs is a crucial factor for defining the properties of these materials. Here, we report for the first time a direct three-dimensional (3D) real-space analysis of individual CMFs and their networks formed after ultrahigh-shear-induced transient deagglomeration and self-assembly in a solvent. Using point-scanning confocal microscopy combined with tracking the centerlines of the fibrils and their junctions by a stretching open active contours method, we reveal that dispersions of the native CMFs assemble into highly heterogeneous networks of individual fibrils and bundles. The average network mesh size decreases with increasing CMF volume fraction. The cross-sectional width and the average length between the twists in the ribbon-shaped CMFs are directly determined and compared well with that of fibrils in the dried state. Finally, the generality of the fluorescent labeling and imaging approach on other CMF sources is illustrated. The unique ability to quantify in situ the multiscale structure in CMF dispersions provides a powerful tool for the correlation of process–structure–property relationship in cellulose-containing composites and dispersions.
MEME - Toward a Method for EMotions Extraction from GitHub

Software engineering researchers are increasingly interested in the role of emotion during software development. While general tools are available to extract emotions from textual data, these perform poorly in the domain of software engineering. Hence, this paper develops MEME – a Method for EMotion Extraction. Using GHtorrent and GitHub as data sources, the paper presents an implementation of the method. The evaluation results suggest a better performance of MEME in contrast to Syuzhet R package emotion analysis.
Optimizing a jump-diffusion model of a starving forager

We analyze the movement of a starving forager on a one-dimensional periodic lattice, where each location contains one unit of food. As the forager lands on sites with food, it consumes the food, leaving the sites empty. If the forager lands consecutively on $s$ empty sites, then it will starve. The forager has two modes of movement: it can either diffuse, by moving with equal probability to adjacent sites on the lattice, or it can jump to a uniformly randomly chosen site on the lattice. We show that the lifetime $T$ of the forager in either paradigm can be approximated by the sum of the cover time $\tau_{\rm cover}$ and the starvation time $s$, when $s$ far exceeds the number $n$ of lattice sites. Our main findings focus on the hybrid model, where the forager has a probability of either jumping or diffusing. The lifetime of the forager varies non-monotonically according to $p_j$, the probability of jumping. By examining a small system, analyzing a heuristic model, and using direct numerical simulation, we explore the tradeoff between jumps and diffusion, and show that the strategy that maximizes the forager lifetime is a mixture of both modes of movement.
Transgenic American Chestnuts Do Not Inhibit Germination of Native Seeds or Colonization of Mycorrhizal Fungi

The American chestnut (Castanea dentata) was once an integral part of eastern United States deciduous forests, with many environmental, economic, and social values. This ended with the introduction of an invasive fungal pathogen that wiped out over three billion trees. Transgenic American chestnuts expressing a gene for oxalate oxidase successfully tolerate infections by this blight fungus, but potential non-target environmental effects should be evaluated before new restoration material is released. Two greenhouse bioassays evaluated belowground interactions between transgenic American chestnuts and neighboring organisms found in their native ecosystems. Potential allelopathy was tested by germinating several types of seeds, all native to American chestnut habitats, in the presence of chestnut leaf litter. Germination was not significantly different in terms of number of seeds germinated or total biomass of germinated seedlings in transgenic and non-transgenic leaf litter. Separately, ectomycorrhizal associations were observed on transgenic and non-transgenic American chestnut roots using field soil inoculum. Root tip colonization was consistently high (>90% colonization) on all plants and not significantly different between any tree types. These observations on mycorrhizal fungi complement previous studies performed on older transgenic lines which expressed oxalate oxidase at lower levels. Along with other environmental impact comparisons, these conclusions provide further evidence that transgenic American chestnuts are not functionally different with regard to ecosystem interactions than non-transgenic American chestnuts.
VIPTRA: Visualization and Interactive Processing on Big Trajectory Data

Massive trajectory data is being collected and used widely in many applications such as transportation, location-based services, and urban computing. As a result, abundant methods and systems have been proposed for managing and processing trajectory data. However, it remains difficult for users to interact well with data management and processing, due to the lack of efficient data processing methods and effective visualization techniques for big trajectory data. In this demonstration, we present a new framework, VIPTRA, to process big trajectory data visually and interactively. VIPTRA builds upon UlTraMan, a distributed in-memory system for big trajectory data, and thus, it takes advantage of its capability of high performance. The demonstration shows the efficiency of data processing and user-friendly visualization and interaction techniques provided in VIPTRA, via several scenarios of visual analysis and trajectory editing tasks.
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation

The KNN approach, which is widely used in recommender systems because of its efficiency, robustness and interpretability, is proposed for session-based recommendation recently and outperforms recurrent neural network models. It captures the most recent co-occurrence information of items by considering the interaction time. However, it neglects the co-occurrence information of items in the historical behavior which is interacted earlier and cannot discriminate the impact of items and sessions with different popularity. Due to these observations, this paper presents a new contextual KNN approach to address these issues for session-based recommendation. Specifically, a diffusion-based similarity method is proposed for considering the popularity of vertices in session-item bipartite network, and a candidate selection method is proposed to capture the items that are co-occurred with different historical clicked items in the same session efficiently. Comprehensive experiments are conducted to demonstrate the effectiveness of our KNN approach over the state-of-the-art KNN approach for session-based recommendation on three benchmark datasets.
IGF1 Gene Is Associated With Triglyceride Levels In Subjects With Family History Of Hypertension From The SAPPHIRe And TWB Projects

Chromosome 12q23-q24 has been linked to triglyceride (TG) levels by previous linkage studies, and it contains the Insulin-like growth factor 1 (IGF1) gene. We investigated the association between IGF1 and TG levels using two independent samples collected in Taiwan. First, based on 954 siblings in 397 families from the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe), we found that rs978458 was associated with TG levels (β = -0.049, p = 0.0043) under a recessive genetic model. Specifically, subjects carrying the homozygous genotype of the minor allele had lower TG levels, compared with other subjects. Then, a series of stratification analyses in a large sample of 13,193 unrelated subjects from the Taiwan biobank (TWB) project showed that this association appeared in subjects with a family history (FH) of hypertension (β = -0.045, p = 0.0000034), but not in subjects without such an FH. A re-examination of the SAPPHIRe sample confirmed that this association appeared in subjects with an FH of hypertension (β = -0.068, p = 0.0025), but not in subjects without an FH. The successful replication in two independent samples indicated that IGF1 is associated with TG levels in subjects with an FH of hypertension in Taiwan.
Serological Detection of Antibodies against Anaplasma spp. in Cattle Reared in the Gyeongsangbuk-do, Korea

Anaplasmosis is a tick-borne, non-contagious, zoonotic disease caused by Anaplasma spp. , which include Anaplasma marginale, A. centrale, A. phagocytophilum, A. platys, A. ovis, and A. bovis. Recently, in Korea, the prevalence of Anaplasma spp. has been investigated in some animals, such as dogs, horses, goats, cats, and Korean water deer. In cattle, A. marginale is the most virulent species and regarded as the typical type of species. However, data on the seroprevalence of Anaplasma spp. in cattle in Korea during the last decade is limited. This study was designed to investigate the seroprevalence of bovine anaplasmosis in Korea. From 2010 to 2013, blood samples were collected from 568 cattle. Forty animals (7.0%) tested seropositive for Anaplasma spp. by cELISA. Despite that current bovine anaplasmosis seropositivity rate in the Gyeongsangbuk-do is lower than those in tropical countries, anaplasmosis needs to be regarded as a concerning disease. The identification of the specific Anaplasma species infecting cattle in this province requires additional molecular studies. Moreover, further monitoring and control programs for bovine anaplasmosis is required, and the information from this study will be beneficial to develop these programs.
The Willingness to Intervene in Cases of Intimate Partner Violence Against Women (WI-IPVAW) Scale: Development and Validation of the Long and Short Versions

Willingness to intervene when one becomes aware of a case of intimate partner violence against women (IPVAW) reflects the level of tolerance and acceptance of this type of violence in society. Increasing the likelihood of intervention to help victims of IPVAW is also a target for prevention strategies aiming to increase informal social control of IPVAW. In this study, we present the development and validation of the Willingness to Intervene in Cases of Intimate Partner Violence (WI-IPVAW) scale. We report data for both the long and short versions of the scale. We analyzed the latent structure, the reliability and validity of the WI-IPVAW across four samples (N = 1648). Factor analyses supported a bifactor model with a general non-specific factor expressing willingness to intervene in cases of IPVAW, and three specific factors reflecting different intervention preferences: a preference for setting the law enforcement process in motion (“calling the cops” factor), a preference for personal intervention (“personal involvement” factor), and a preference for non-intervention (“not my business” factor). Configural, metric, and partial scalar invariance across genders were supported. Two short versions of the scale, with nine and six items, respectively, were constructed on the base of quantitative and qualitative criteria. The long and short versions of the WI-IPVAW demonstrated both high reliability and construct validity, as they were strongly related to the acceptability of IPVAW, victim-blaming attitudes, perceived severity of IPVAW, and hostile sexism. These results confirm that both the long and short versions of the WI-IPVAW scale are psychometrically sound instruments to analyze willingness to intervene in cases of IPVAW in different settings and with different research needs (e.g., long versions for clinical and research settings, and short versions for large population surveys). The WI-IPVAW is also useful for assessing prevention policies and public education campaigns design to promote a more responsive social environment in cases of IPVAW, thus contributing to deter and reduce this major social and public health problem.
Mining Semantic Loop Idioms

To write code, developers stitch together patterns, like API protocols or data structure traversals. Discovering these patterns can identify inconsistencies in code or opportunities to replace these patterns with an API or a language construct. We present coiling, a technique for automatically mining code for semantic idioms: surprisingly probable, semantic patterns. We specialize coiling for loop idioms, semantic idioms of loops. First, we show that automatically identifiable patterns exist, in great numbers, with a large-scale empirical study of loops over 25MLOC. We find that most loops in this corpus are simple and predictable: 90 percent have fewer than 15LOC and 90 percent have no nesting and very simple control. Encouraged by this result, we then mine loop idioms over a second, buildable corpus. Over this corpus, we show that only 50 loop idioms cover 50 percent of the concrete loops. Our framework opens the door to data-driven tool and language design, discovering opportunities to introduce new API calls and language constructs. Loop idioms show that LINQ would benefit from an Enumerate operator. This can be confirmed by the exitence of a StackOverflow question with 542k views that requests precisely this feature.
Antidepressant-like Effects of p-Coumaric Acid on LPS-induced Depressive and Inflammatory Changes in Rats

Depression causes mental and physical changes which affect quality of life. It is estimated to become the second most prevalent disease, but despite its commonness, the pathophysiology of depression remains unclear and medicine is not sufficiently protective. p-Coumaric acid (p-CA) is a dietary phenolic acid which has been proven to have antifungal, anti-HIV, anti-melanogenic, antioxidant and anti-inflammatory effects. Considering these effects, we investigated whether p-CA can prevent depressive symptoms by reducing inflammatory cytokines in animals injected with lipopolysaccharide (LPS). Changes in despair-related behaviors, inflammatory cytokines, neurotrophic factors and synaptic activity were measured. In these animals, p-CA improved despair-related behavioral symptoms induced by LPS in the forced swim test (FST), tail suspension test (TST) and sucrose splash test (SST). p-CA also prevented the increase of inflammatory cytokines in the hippocampus such as cycloxigenase-2 and tumor necrosis factor-α due to LPS. Similarly, it prevented the reduction of brain-derived neurotrophic factor (BDNF) by LPS. Electrophysiologically, p-CA blocked the reduction of long-term depression in LPS-treated organotypic tissue slices. In conclusion, p-CA prevented LPS-induced depressive symptoms in animals, as determined by behavioral, biochemical and electrophysiological measures. These findings suggest the potential use of p-CA as a preventive and therapeutic medicine for depression.
Phase-Sensitive Joint Learning Algorithms for Deep Learning-Based Speech Enhancement

This letter presents a phase-sensitive joint learning algorithm for single-channel speech enhancement. Although a deep learning framework that estimates the time-frequency (T-F) domain ideal ratio masks demonstrates a strong performance, it is limited in the sense that the enhancement process is performed only in the magnitude domain, while the phase spectra remain unchanged. Thus, recent studies have been conducted to involve phase spectra in speech enhancement systems. A phase-sensitive mask (PSM) is a T-F mask that implicitly represents phase-related information. However, since the PSM has an unbounded value, the networks are trained to target its truncated values rather than directly estimating it. To effectively train the PSM, we first approximate it to have a bounded dynamic range under the assumption that speech and noise are uncorrelated. We then propose a joint learning algorithm that trains the approximated value through its parameterized variables in order to minimize the inevitable error caused by the truncation process. Specifically, we design a network that explicitly targets three parameterized variables: 1) speech magnitude spectra; 2) noise magnitude spectra; and 3) phase difference of clean to noisy spectra. To further improve the performance, we also investigate how the dynamic range of magnitude spectra controlled by a warping function affects the final performance in joint learning algorithms. Finally, we examined how the proposed additional constraint that preserves the sum of the estimated speech and noise power spectra affects the overall system performance. The experimental results show that the proposed learning algorithm outperforms the conventional learning algorithm with the truncated phase-sensitive approximation.
Laika: Efficient In-Place Scheduling for 3D Mesh Graph Computations

Scientific computing problems are frequently solved using data-graph computations -- algorithms that perform local updates on application-specific data associated with vertices of a graph, over many time steps. The data-graph in such computations is commonly a mesh graph, where vertices have positions in 3D space, and edges connect physically nearby vertices. A scheduler controls the parallel execution of the algorithm. Two classes of parallel schedulers exist: double-buffering and in-place. Double-buffering schedulers do not incur synchronization overheads due to an absence of read-write conflicts, but require two copies of the vertices, as well as a higher iteration count due to a slower convergence rate. Computations for which this difference in convergence rate is significant (e.g., multigrid method) are frequently performed using an in-place scheduler, which incurs synchronization overheads to avoid read-write conflicts on the single copy of vertex data. We present Laika, a deterministic in-place scheduler we created using a principled three-step design strategy for high-performance schedulers. Laika reorders the input graph using a Hilbert space-filling curve to improve cache locality and minimizes parallel coordination overhead by explicitly curbing excess execution parallelism. Consequently, Laika has significantly lower scheduling overhead than alternative in-place schedulers and is even faster per iteration than the parallel double-buffered implementation on a reordered input graph. We derive an improved bound on the expected number of cache misses incurred during a traversal of a graph reordered using a space-filling curve. We also prove that on a mesh graph G = (V, E), Laika performs O(|V| + |E|) total work and achieves linear expected speedup with P = O(|V| / log^2 |V|) workers. On 48 cores, Laika yields 38.4x parallel speedup and empirically fares well against comparably well-engineered alternatives: it runs 6.97--12.60 times faster in geometric mean over a suite of input graphs than other parallel schedulers and 222.57 times faster than the baseline serial implementation.
Feature-Based Transfer Learning Based on Distribution Similarity

Transfer learning has been found helpful at enhancing the target domain’s learning process by transferring useful knowledge from other different but related source domains. In many applications, however, collecting and labeling target information is not only very difficult but also expensive. At the same time, considerable prior experience in this regard exists in other application domains. This paper proposes a feature-based transfer learning method based on distribution similarity that aims at the partial overlap of features between two domains. The non-overlapping features are completed by leveraging the distribution similarity of other features within the source domain. Features of the two domains are then reweighted in accordance with the distribution similarity between the source and target domains. This, in turn, decreases the distribution discrepancy between the two domains, therefore achieving the desired feature transfer. Results of the experiments performed on Facebook and Sina Microblog data sets demonstrate that the proposed method is capable of effectively enhancing the accuracy of the prediction function.