Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal diversity of Cerambycidae (Coleoptera) is more complex than thought: evidence from a tropical dry forest of Mexico

Global climate change is expected to affect temperature and precipitation patterns worldwide, which in turn is likely to affect insect phenology, distribution and diversity. To improve our understanding of such processes, it is important to understand how insects may respond to changes in seasonality, and how these affect their activity, patterns of distribution and species richness. The tropical dry forest (TDF) is a highly seasonal ecosystem, for which two seasons are commonly described (rainy and dry) and there is a lack of information on the combined effect of both precipitation and temperature on the insect communities. In order to evaluate the seasonal patterns in the community of Cerambycidae in a TDF, historical climatic variables were obtained, and an annual sampling of the family was carried out, using three collection techniques. We found that the Cerambycidae family showed a more complex response to climate, than simply the rainy and dry season of the year. The relationship between diversity and composition of cerambycids with changes in temperature and precipitation showed four seasonal communities which were synchronized with phenological processes of the TDF. Climate change could reduce biodiversity, causing seasonal patterns to lose complexity, either because the climatic characteristics of a season disappear and/or because the duration of a season expands, these changes will modify the ecological processes of the TDF, since they would generate changes in the flora and fauna associated with the different seasons.
Effects of particle size of ground alfalfa hay on caecal bacteria and archaea populations of rabbits

This work was aimed to investigate the effects of the different particle size of ground alfalfa hay on caecal microbial and archeal communities of rabbits. One hundred-twenty New Zealand rabbits (950.3 ± 8.82 g) were allocated into four treatments, with five replicates in each treatment and six rabbits in each replicate. The particle sizes of the alfalfa meal in the four treatment diets were 2,500, 1,000, 100 and 10 µm respectively, while the other ingredients were ground through a 2.5 mm sieve. High-throughput sequencing technology was applied to examine the differences in bacteria and methanogenic archaea diversity in the caecum of the four treatment groups of rabbits. A total of 745,946 bacterial sequences (a mean of 31,081 ± 13,901 sequences per sample) and 539,227 archaeal sequences (a mean of 22,468 ± 2,443 sequences per sample) were recovered from twenty-four caecal samples, and were clustered into 9,953 and 2,246 OTUs respectively. A total of 26 bacterial phyla with 465 genera and three archaeal phyla with 10 genera were identified after taxonomic summarization. Bioinformatic analyses illustrated that Firmicutes (58.69% ∼ 68.50%) and Bacteroidetes (23.96% ∼ 36.05%) were the two most predominant bacterial phyla and Euryarchaeota (over 99.9%) was the most predominant archaeal phyla in the caecum of all rabbits. At genus level, as the particle size of alfalfa decreased from 2,500 to 10 µm, the relative abundances of Ruminococcaceae UCG-014 (P < 0.001) and Lactobacillus (P = 0.043) were increased and Ruminococcaceae UCG-005 (P = 0.012) was increased first and then decreased when the alfalfa particle size decreased, while Lachnospiraceae NK4A136 group (P = 0.016), Ruminococcaceae NK4A214 (P = 0.044), Christensenellaceae R-7 group (P = 0.019), Lachnospiraceae other (Family) (P = 0.011) and Ruminococcaceae UCG-013 (P = 0.021) were decreased. The relative abundance of Methanobrevibacter was increased from 62.48% to 90.40% (P < 0.001), whereas the relative abundance of Methanosphaera was reduced from 35.47% to 8.62% (P < 0.001). In conclusion, as the particle size of alfalfa meal decreased, both the bacterial and archaeal population in the caecum of rabbit experienced alterations, however archaea response earlier than bacteria to the decrease of alfalfa meal particle size.
The Impact of Antithrombotic Regimens on Clinical Outcomes After Endovascular Intervention and Bypass Surgery for Infrapopliteal Artery Disease

Endovascular intervention and bypass surgery are the main options of treatments for infrapopliteal artery disease. Although post-intervention treatment with antiplatelet (AP) and/or anticoagulant (AC) drugs has reduced morbidity and mortality rates from cardiovascular complications; the ideal antithrombotic treatment regimen is unknown. The aim of this review was to compare the efficacy and safety of various anticoagulation and/or AP therapy regimens in patients undergoing below-knee endovascular treatment for infrapopliteal artery disease. We reviewed published literature in PubMed and Google Scholar, and Cochrane, evaluating efficacy and safety outcomes after antithrombotic treatment following endovascular intervention or bypass surgery in patients with infrapopliteal artery disease. We extracted relevant efficacy and safety data with related statistics from each study. We found that AP treatment should be administered to patients receiving endovascular therapy or bypass. We did not find superior effects for dual AP treatment (DAPT) over mono-AP therapy (MAPT) for endovascular intervention or bypass surgery with venous graft, suggesting that MAPT suffices for these groups. Also, aspirin + clopidogrel was effective over aspirin alone for prosthetic, but not venous graft, albeit higher non-severe bleeding incidences, suggesting a potential benefit of this regime for below-knee prosthetic graft. AP + AC yielded superior results compared to AP following endovascular procedure and bypass surgery, suggesting the potential benefit of this regime in the absence of contraindications. More prospective studies with large number of patients are warranted to identify the best treatment for infrapopliteal artery diseases.
GhostAR: A Time-space Editor for Embodied Authoring of Human-Robot Collaborative Task with Augmented Reality

We present GhostAR, a time-space editor for authoring and acting Human-Robot-Collaborative (HRC) tasks in-situ. Our system adopts an embodied authoring approach in Augmented Reality (AR), for spatially editing the actions and programming the robots through demonstrative role-playing. We propose a novel HRC workflow that externalizes user's authoring as demonstrative and editable AR ghost, allowing for spatially situated visual referencing, realistic animated simulation, and collaborative action guidance. We develop a dynamic time warping (DTW) based collaboration model which takes the real-time captured motion as inputs, maps it to the previously authored human actions, and outputs the corresponding robot actions to achieve adaptive collaboration. We emphasize an in-situ authoring and rapid iterations of joint plans without an offline training process. Further, we demonstrate and evaluate the effectiveness of our workflow through HRC use cases and a three-session user study.
Distrust issues in business-to-business e-procurement decisions

Purpose Despite the important role of distrust in predicting the intention to use, existing literature has not studied the impact of distrust on the buyer’s integration with suppliers and their e-procurement usage in the B2B context, creating major gaps in the e-procurement theory. The purpose of this paper is to fill the gaps in the literature by examining the role and impact of distrust in the e-procurement context. Design/methodology/approach Data were collected through a web-based survey of purchasing professionals in the USA. ANOVA was used to compare the level of distrust among companies. In addition, the measurement model and hypotheses were tested using the structural equation modeling method. Findings Empirical findings indicate if buyers have uncertainty or negative expectation on the reliability and capability of the supplier in e-procurement systems, they would tend to hesitate to use e-procurement for purchasing. Furthermore, distrust also inhibits buyers from strengthening the supplier integration through sharing operational and logistics information and collaborating in new product development and purchasing process. Research limitations/implications This paper discovers the vital role of distrust in the e-procurement context. Distrust has a significant impact on a company’s cooperation and commitment with business partners. Additionally, to improve the supplier integration, it is important not only to use e-procurement applications but also to have a mechanism to lower the buyers’ distrust level in e-procurement systems. Practical implications Realizing the important role of distrust, e-procurement vendors could improve the supplier selection process by allowing buyers to review the supplier’s reliability and capability, and to interact with the supplier before making any online purchase. Vendors could also build a better supplier verification system and collaborative mechanism to reduce buyers’ distrust issues. Originality/value Despite the important role of distrust in predicting the intention to use, existing literature has not studied the impact of distrust on the buyer’s integration with suppliers and their e-procurement usage in the B2B context, creating major gaps in the e-procurement theory. This paper fills the gaps in the literature by examining the role and impact of distrust in the e-procurement context.
A New Large-scale Food Image Segmentation Dataset and Its Application to Food Calorie Estimation Based on Grains of Rice

To estimate food calorie accurately from food images, accurate food image segmentation is needed. So far no large-scale food image segmentation datasets which have pixel-wise labels exists. In this paper, we added segmentation masks to the food images in the existing dataset, UEC-Food100, semi-automatically. To estimate segmentation masks, we revised bounding boxes included in the UEC-Food100 dataset so that bounding boxes bounds not dish regions but only food regions. As results, by applying GrubCut, we obtained good segmentation masks, and we checked and corrected 1000 images of them if needed by hand for the testing masks. We trained segmentation networks with the newly-created food image masks. As results, segmentation accuracy was much improved, which is expected to bring more accurate food calorie estimation. In addition, we propose a new method on food calorie estimation using grains of steamed rice which are typically contained in Japanese foods instead of a reference card. By the experiments, we show real food size can be estimated from rice images, which helps accurate food calorie estimation.
Opisthenar: Hand Poses and Finger Tapping Recognition by Observing Back of Hand Using Embedded Wrist Camera

We introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.
DNS Observatory: The Big Picture of the DNS

The Domain Name System (DNS) is thought of as having the simple-sounding task of resolving domains into IP addresses. With its stub resolvers, different layers of recursive resolvers, authoritative nameservers, a multitude of query types, and DNSSEC, the DNS ecosystem is actually quite complex. In this paper, we introduce DNS Observatory: a new stream analytics platform that provides a bird's-eye view on the DNS. As the data source, we leverage a large stream of passive DNS observations produced by hundreds of globally distributed probes, acquiring a peak of 200 k DNS queries per second between recursive resolvers and authoritative nameservers. For each observed DNS transaction, we extract traffic features, aggregate them, and track the top-k DNS objects, e.g., the top authoritative nameserver IP addresses or the top domains. We analyze 1.6 trillion DNS transactions over a four month period. This allows us to characterize DNS deployments and traffic patterns, evaluate its associated infrastructure and performance, as well as gain insight into the modern additions to the DNS and related Internet protocols. We find an alarming concentration of DNS traffic: roughly half of the observed traffic is handled by only 1 k authoritative nameservers and by 10 AS operators. By evaluating the median delay of DNS queries, we find that the top 10 k nameservers have indeed a shorter response time than less popular nameservers, which is correlated with less router hops. We also study how DNS TTL adjustments can impact query volumes, anticipate upcoming changes to DNS infrastructure, and how negative caching TTLs affect the Happy Eyeballs algorithm. We find some popular domains with a a share of up to 90 % of empty DNS responses due to short negative caching TTLs. We propose actionable measures to improve uncovered DNS shortcomings.
Performance and protection in the ZoFS user-space NVM file system

Non-volatile memory (NVM) can be directly accessed in user space without going through the kernel. This encourages several recent studies on building user-space NVM file systems. However, for the sake of file system protection, none of the existing file systems grant user-space file system libraries with direct control over both metadata and data of the NVM, leaving fast NVM resources underexploited. Based on the observation that applications tend to group files with similar access permissions within the same directory and permission changes are rare operations, this paper proposes a new abstraction called coffer, which is a collection of isolated NVM resources, and show its merits on building a performant and protected NVM file system in user space. The key idea is to separate NVM protection from management via coffers so that user-space libraries can take full control of NVM within a coffer while the kernel guarantees strict isolation among coffers. Based on coffers, we build an NVM file system architecture to bring the high performance of NVM to unmodified dynamically linked applications and facilitate the development of performant and flexible user-space NVM file system libraries. With an example file system called ZoFS, we show that user-space file systems built upon coffers can outperform existing NVM file systems in both benchmarks and real-world applications.
Reducing Permission Requests in Mobile Apps

Users of mobile apps sometimes express discomfort or concerns with what they see as unnecessary or intrusive permission requests by certain apps. However encouraging mobile app developers to request fewer permissions is challenging because there are many reasons why permissions are requested; furthermore, prior work [25] has shown it is hard to disambiguate the purpose of a particular permission with high certainty. In this work we describe a novel, algorithmic mechanism intended to discourage mobile-app developers from asking for unnecessary permissions. Developers are incentivized by an automated alert, or "nudge", shown in the Google Play Console when their apps ask for permissions that are requested by very few functionally-similar apps---in other words, by their competition. Empirically, this incentive is effective, with significant developer response since its deployment. Permissions have been redacted by 59% of apps that were warned, and this attenuation has occurred broadly across both app categories and app popularity levels. Importantly, billions of users' app installs from the Google Play have benefited from these redactions.
A proactive caching scheme based on content concentration in content-centric networks

: Content-Centric Networking (CCN) provides a new networking paradigm to overcome the challenges of the current Internet and better support the content-oriented services. In-network caching is one of the core technologies for optimizing content distribution and has been attracting ever increasing attention. In the current CCN caching schemes, the caching decision is only implemented during the content response process after the content which matches the user request is found, which lacks the capacity of proactive content advancement. This paper borrows the idea of molecular diffusion and introduces the conception of content concentration into the caching decision to analytically model the user demands for different contents in different network regions, and then a proactive caching scheme based on content concentration is proposed. In this scheme, the content replica can be proactively pushed from the high concentration region to the low concentration one to realize the fast deployment of content cache replicas and the dynamic adjustment of the cache location. Furthermore, a probabilistic content placement method is also implemented by synthetically considering the content popularity. The simulation results show that this scheme can effectively improve the cache hit ratio and shorten the delay involved in content search and response.
CrashTuner: detecting crash-recovery bugs in cloud systems via meta-info analysis

Crash-recovery bugs (bugs in crash-recovery-related mechanisms) are among the most severe bugs in cloud systems and can easily cause system failures. It is notoriously difficult to detect crash-recovery bugs since these bugs can only be exposed when nodes crash under special timing conditions. This paper presents CrashTuner, a novel fault-injection testing approach to combat crash-recovery bugs. The novelty of CrashTuner lies in how we identify fault-injection points (crash points) that are likely to expose errors. We observe that if a node crashes while accessing meta-info variables, i.e., variables referencing high-level system state information (e.g., an instance of node or task), it often triggers crash-recovery bugs. Hence, we identify crash points by automatically inferring meta-info variables via a log-based static program analysis. Our approach is automatic and no manual specification is required. We have applied CrashTuner to five representative distributed systems: Hadoop2/Yarn, HBase, HDFS, ZooKeeper, and Cassandra. CrashTuner can finish testing each system in 17.39 hours, and reports 21 new bugs that have never been found before. All new bugs are confirmed by the original developers and 16 of them have already been fixed (14 with our patches). These new bugs can cause severe damages such as cluster down or start-up failures.
Learning Regexes to Extract Router Names from Hostnames

We present the design, implementation, evaluation, and validation of a system that automatically learns to extract router names (router identifiers) from hostnames stored by network operators in different DNS zones, which we represent by regular expressions (regexes). Our supervised-learning approach evaluates automatically generated candidate regexes against sets of hostnames for IP addresses that other alias resolution techniques previously inferred to identify interfaces on the same router. Conceptually, if three conditions hold: (1) a regex extracts the same value from a set of hostnames associated with IP addresses on the same router; (2) the value is unique to that router; and (3) the regex extracts names for multiple routers in the suffix, then we conclude the regex accurately represents the naming convention for the suffix. We train our system using router aliases inferred from active probing to learn regexes for 2550 different suffixes. We then demonstrate the utility of this system by using the regexes to find 105% additional aliases for these suffixes. Regexes inferred in IPv4 perfectly predict aliases for ≈85% of suffixes with IPv6 aliases, i.e., IPv4 and IPv6 addresses representing the same underlying router, and find 9.0 times more routers in IPv6 than found by prior techniques.
A Secure Model For Efficient Live Migration of Containers

Cloud services have become increasingly widespread in the past decade due to their ability to reduce the complexity and the cost of managing computers and networks. Cloud applications are run in virtualized environments such as virtual machines and containers to be able to allocate resources in an inexpensive manner. Both of these approaches require effective resource utilization, for which an important enabling technology is live migration, which involves moving a service from one host to another with the minimum possible downtime. Live migration is also required for system maintenance, load balancing, and protecting services from attacks through moving target defense. While migrating a service, the system should not be vulnerable to attacks. In this work, we propose a secure model for efﬁcient live migration of containers. Because the applications are isolated from each other while running in Docker containers, checkpointing method was used to generate required migration data. In our proposed model, we provide security of the migration data using secure authentication, and ensuring all connections between the nodes are protected to provide communication security, making the system protected against migration attacks. The efﬁciency of the migration system designed based on the proposed model has been proven on stateless and stateful sample applications. Experiments with applications running on the Docker container platform demonstrate that the proposed approach achieves signiﬁcantly better performance than its virtual machine live migration counterpart.
CircuitStyle: A System for Peripherally Reinforcing Best Practices in Hardware Computing

Instructors of hardware computing face many challenges including maintaining awareness of student progress, allocating their time adequately between lecturing and helping individual students, and keeping students engaged even while debugging problems. Based on formative interviews with 5 electronics instructors, we found that many circuit style behaviors could help novice users prevent or efficiently debug common problems. Drawing inspiration from the software engineering practice of coding style, these circuit style behaviors consist of best-practices and guidelines for implementing circuit prototypes that do not interfere with the functionality of the circuit, but help a circuit be more readable, less error-prone, and easier to debug. To examine if these circuit style behaviors could be peripherally enforced, aid an in-person instructor's ability to facilitate a workshop, and not monopolize instructor's attention, we developed CircuitStyle, a teaching aid for in-person hardware computing workshops. To evaluate the effectiveness of our tool, we deployed our system in an in-person maker-space workshop. The instructor appreciated CircuitStyle's ability to provide a broad understanding of the workshop's progress and the potential for our system to help instructors of various backgrounds better engage and understand the needs of their classroom.