Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of different deep-sea coral habitats on sediment macrofaunal community structure and function

Deep-sea corals can create a highly complex, three-dimensional structure that facilitates sediment accumulation and influences adjacent sediment environments through altered hydrodynamic regimes. Infaunal communities adjacent to different coral types, including reef-building scleractinian corals and individual colonies of octocorals, are known to exhibit higher macrofaunal densities and distinct community structure when compared to non-coral soft-sediment communities. However, the coral types have different morphologies, which may modify the adjacent sediment communities in discrete ways. Here we address: (1) how infaunal communities and their associated sediment geochemistry compare among deep-sea coral types (Lophelia pertusa, Madrepora oculata, and octocorals) and (2) do infaunal communities adjacent to coral habitats exhibit typical regional and depth-related patterns observed in the Gulf of Mexico (GOM). Sediment push cores were collected to assess diversity, composition, numerical abundance, and functional traits of macrofauna (>300 µm) across 450 kilometers in the GOM at depths ranging from 263–1,095 m. Macrofaunal density was highest in L. pertusa habitats, but similar between M. oculata and octocorals habitats. Density overall exhibited a unimodal relationship with depth, with maximum densities between 600 and 800 m. Diversity and evenness were highest in octocoral habitats; however, there was no relationship between diversity and depth. Infaunal assemblages and functional traits differed among coral habitats, with L. pertusa habitats the most distinct from both M. oculata and octocorals. These patterns could relate to differences in sediment geochemistry as L. pertusa habitats contained high organic carbon content but low proportions of mud compared to both M. oculata and octocoral habitats. Distance-based linear modeling revealed depth, mud content, and organic carbon as the primary factors in driving coral infaunal community structure, while geographic location (longitude) was the primary factor in functional trait composition, highlighting both the location and ecological differences of L. pertusa habitats from other coral habitats. Enhanced habitat structural complexity associated with L. pertusa and differences in localized hydrodynamic flow may contribute to the dissimilarities in the communities found among the coral types. Our results suggest a decoupling for infaunal coral communities from the typical depth-related density and diversity patterns present throughout soft-sediment habitats in the GOM, highlighting the importance of deep-sea corals in structuring unique communities in the nearby benthos.
Collective Matrix Completion

Matrix completion aims to reconstruct a data matrix based on observations of a small number of its entries. Usually in matrix completion a single matrix is considered, which can be, for example, a rating matrix in recommendation system. However, in practical situations, data is often obtained from multiple sources which results in a collection of matrices rather than a single one. In this work, we consider the problem of collective matrix completion with multiple and heterogeneous matrices, which can be count, binary, continuous, etc. We first investigate the setting where, for each source, the matrix entries are sampled from an exponential family distribution. Then, we relax the assumption of exponential family distribution for the noise and we investigate the distribution-free case. In this setting, we do not assume any specific model for the observations. The estimation procedures are based on minimizing the sum of a goodness-of-fit term and the nuclear norm penalization of the whole collective matrix. We prove that the proposed estimators achieve fast rates of convergence under the two considered settings and we corroborate our results with numerical experiments.
GUIFetch: Supporting App Design and Development through GUI Search

A typical way to design and develop a mobile app is to sketch the graphical user interfaces (GUIs) for the different screens in the app and then create actual GUIs from these sketches. Doing so involves identifying which layouts to use, which widgets to add, and how to configure and connect the different pieces of the GUI. To help with this difficult and time-consuming task, we propose GUIFetch, a technique that takes as input the sketch for an app and leverages the growing number of open source apps in public repositories to identify apps with GUIs and transitions that are similar to those in the provided sketch. GUIFetch first searches public repositories to find potential apps using keyword matching. It then builds models of the identified apps’ screens and screen transitions using a combination of static and dynamic analyses and computes a similarity metric between the models and the provided sketch. Finally, GUIFetch ranks the identified apps (or parts thereof) based on their computed similarity value and produces a visual ranking of the results together with the code of the corresponding apps. We implemented GUIFetch for Android apps and evaluated it through user studies involving different types of apps.
A Hybrid of Deep Audio Feature and i-vector for Artist Recognition

Artist recognition is a task of modeling the artist's musical style. This problem is challenging because there is no clear standard. We propose a hybrid method of the generative model i-vector and the discriminative model deep convolutional neural network. We show that this approach achieves state-of-the-art performance by complementing each other. In addition, we briefly explain the advantages and disadvantages of each approach.
Deep Censored Learning of the Winning Price in the Real Time Bidding

We generalize the winning price model to incorporate the deep learning models with different distributions and propose an algorithm to learn from the historical bidding information, where the winning price are either observed or partially observed. We study if the successful deep learning models of the click-through rate can enhance the prediction of the winning price or not. We also study how different distributions of winning price can affect the learning results. Experiment results show that the deep learning models indeed boost the prediction quality when they are learned on the historical observed data. In addition, the deep learning models on the unobserved data are improved after learning from the censored data. The main advantage of the proposed generalized deep learning model is to provide more flexibility to model the winning price and improve the performance in consideration of the possibly various winning price distributions and various model structures in practice.
A Sample Aggregation Approach to Experiences Replay of Dyna-Q Learning

In a complex environment, the learning efficiency of reinforcement learning methods always decreases due to large-scale or continuous spaces problems, which can cause the well-known curse of dimensionality. To deal with this problem and enhance learning efficiency, this paper introduces an aggregation method by using framework of sample aggregation based on Chinese restaurant process (CRP), named FSA-CRP, to cluster experiential samples, which is represented by quadruples of the current state, action, next state, and the obtained reward. In addition, the proposed algorithm applies a similarity estimation method, the MinHash method, to calculate the similarity between samples. Moreover, to improve the learning efficiency, the experience sharing Dyna learning algorithm based on samples/clusters prediction method is proposed. While an agent learns the value function of the current state, it acquires clustering results, the value functions of the sample merge with the original as the updated value function of the cluster. In indirect learning (planning) for the Dyna-Q, a learning agent looks for the most likely branches of the constructed FSA-CRP model to raise up learning efficiency. The most likely branches will be selected by an improved action/sample selection algorithm. The algorithm applies the probability that the sample appears in the cluster to select simulated experiences for indirect learning. To verify the validity and applicability of the proposed method, experiments are conducted on a simulated maze and a cart-pole system. The results demonstrate that the proposed method can effectively accelerate the learning process.
Functional Analysis of a Carboxylesterase Gene Associated With Isoprocarb and Cyhalothrin Resistance in Rhopalosiphum padi (L.)

Carboxylesterase (CarE) is an important class of detoxification enzymes involved in insecticide resistance. However, the molecular mechanism of CarE-mediated insecticide resistance in Rhopalosiphum padi, a problematic agricultural pest, remains largely unknown. In the present study, an isoprocarb-resistant (IS-R) strain and a cyhalothrin-resistant (CY-R) strain were successively selected from a susceptible (SS) strain of R. padi. The enzyme activity indicated that enhanced carboxylesterase activity contributes to isoprocarb and cyhalothrin resistance. The expression levels of putative CarE genes were examined and compared among IS-R, CY-R, and SS strains, and only the R. padi carboxylesterase gene (RpCarE) was significantly over expressed in both the IS-R and CY-R strains compared to the SS strain. The coding region of the RpCarE gene was cloned and expressed in Escherichia coli. The purified RpCarE protein was able to catalyze the model substrate, α-naphtyl acetate (Kcat = 5.50 s-1; Km = 42.98 μM). HPLC assay showed that the recombinant protein had hydrolase activity against isoprocarb and cyhalothrin. The modeling and docking analyses consistently indicated these two insecticide molecules fit snugly into the catalytic pocket of RpCarE. Taken together, these findings suggest that RpCarE plays an important role in metabolic resistance to carbamates and pyrethroids in R. padi.
Aiding Comprehension of Unit Test Cases and Test Suites with Stereotype-Based Tagging

Techniques to automatically identify the stereotypes of different software artifacts (e.g., classes, methods, commits) were previously presented. Those approaches utilized the techniques to support comprehension of software artifacts, but those stereotype-based approaches were not designed to consider the structure and purpose of unit tests, which are widely used in software development to increase the quality of source code. Moreover, unit tests are different than production code, since they are designed and written by following different principles and workflows. In this paper, we present a novel approach, called TeStereo, for automated tagging of methods in unit tests. The tagging is based on an original catalog of stereotypes that we have designed to improve the comprehension and navigation of unit tests in a large test suite. The stereotype tags are automatically selected by using static control-flow, data-flow, and API call based analyses. To evaluate the benefits of the stereotypes and the tagging reports, we conducted a study with 46 students and another survey with 25 Apache developers to (i) validate the accuracy of the inferred stereotypes, (ii) measure the usefulness of the stereotypes when writing/understanding unit tests, and (iii) collect feedback on the usefulness of the generated tagging reports.
K8-Scalar: A Workbench to Compare Autoscalers for Container-Orchestrated Database Clusters

Although a considerable amount of research exists on auto-scaling of database clusters, the design of an effective auto-scaling strategy requires fine-grained tailoring towards the specific application scenario. This paper presents an easy-to-use and extensible workbench exemplar, named K8-Scalar (Kube-Scalar), which allows researchers to implement and evaluate different self-adaptive approaches to autoscaling container-orchestrated services. The workbench is based on Docker, a popular technology for easing the deployment of containerized software that also has been positioned as an enabler for reproducible research. The workbench also relies on a container orchestration framework: Kubernetes (K8s), the de-facto industry standard for orchestration and monitoring of elastically scalable container-based services. Finally, it integrates and extends Scalar, a generic testbed for evaluating the scalability of large-scale systems with support for evaluating the performance of autoscalers for database clusters. The paper discusses (i) the architecture and implementation of K8-Scalar and how a particular autoscaler can be plugged in, (ii) sketches the design of a Riemann-based autoscaler for database clusters, (iii) illustrates how to design, setup and analyze a series of experiments to configure and evaluate the performance of this autoscaler for a particular database (i.e., Cassandra) and a particular workload type, and (iv) validates the effectiveness of K8-Scalar as a workbench for accurately comparing the performance of different auto-scaling strategies.
ANEL: Robust Mobile Network Programming Using a Declarative Language

The dynamics of mobile networks make it difficult for mobile apps to deliver a seamless user experience. In particular, intermittent connections and weak signals pose challenges for app developers. While recent network libraries have simplified network programming, much expert knowledge is still required. However, most mobile app developers are relative novices and tend to assume a reliable network connection, paying little attention to handling network errors in programming until users complain and leave bad reviews. We argue that the difficulty of avoiding such software defects can be mitigated through an annotation language that allows developers to declaratively state desired and actual properties of the application, largely without reference to fault-tolerant concepts, much less implementation. A pre-compiler can process these annotations, replacing calls to standard networking libraries with customized calls to a specialized library that enhances the reliability. This paper presents ANEL, a declarative language and middleware for Android that en-ables non-experts. We demonstrate the expressiveness and practicability of ANEL annotation through case studies and usability studies on real-world networked mobile apps. We also show that the ANEL middleware introduces negligible runtime performance overhead.
Scalable Transaction Processing Using Functors

Distributed transactions, which access data items at multiple sites atomically, face well-known scalability challenges. To avoid the high overhead, in prior work Fan et al. proposed Epoch-based Concurrency Control (ECC), which makes transactions visible at epoch boundaries, and presented a system that supports high performance read-only and write-only transactions. However, this idea has a clear difficulty to overcome: the common case of a single transaction that does both reading and writing. This paper proposes ALOHA-DB, a scalable distributed transaction processing system. ALOHA-DB uses a novel paradigm of serializable transaction processing using functors, which conceptually resemble futures in modern programming languages. A functor is a placeholder for the value of a key, which can be computed asynchronously in the future in parallel with other functor computations of the same or other transactions. With multi-versioning in ECC, the functor computations only rely on accessing historical versions, and so the traditional locking mechanism is not needed for concurrency control. Functors elevate ECC to a new level: supporting serializable distributed read-write transactions. This combination of techniques never aborts transactions due to read-write or write-write conflicts, but allows transactions to fail due to logic errors or constraint violations. We used functor-enabled ECC to implement ALOHA-DB and evaluated it using TPC-C and YCSB read-write distributed transactions. Experimental results demonstrate that our system's performance on the TPC-C benchmark is nearly 2 million transactions per second over 20 eight-core virtual machines, which outperforms Calvin, a state-of-the-art transaction processing and replication layer, by one to two orders of magnitude.
A 1.08-nW/kHz 13.2-ppm/°C Self-Biased Timer Using Temperature-Insensitive Resistive Current

This paper proposes a 32.7 kHz self-biased wake-up timer using temperature-insensitive resistive current. A dual-loop structure with supply-regulation loop and frequency-locked loop determines the regulated voltages at both ends of a temperature-compensated resistor and cancels out the amplifier offset voltages, thus generating temperature-insensitive resistive current for the switched capacitor. Moreover, a self-biasing scheme in low-power design reduces the power variation across the temperature range and eliminates the necessity of a sub- $\mu \text{A}$ reference current generator. The proposed timer with its start-up circuit generates <12 ms lock time, which is 20 times less than what obtained without a start-up circuit, and exhibits a long-term frequency stability of 10 ppm and a temperature coefficient of 13.2 ppm/°C with an energy efficiency of 1.08 nW/kHz.
Preventing Utilization of Shared Network Resources by Detecting IP Spoofing Attacks through Validation of source IP Address

Network intruders may spoof IP packets by modifying headers of the IP packets in order to fool people to believe that the transmissions are originating from trusted source. Consequently, various defencing mechanisms have been developed to identify and prevent IP spoofing attacks. However, most of them were implemented on either hosts or routers levels which utilizes a lot of shared resources on the networks during the attacking process. On the contrary, this paper proposes an algorithm for validating source IP address by using Layer Three Switches (L3S) in a Local Area Network (LAN), which means that the IP spoofing attacks will be identified and prevented without utilizing shared network resources. The study employed Mininet network emulator, POX controller, L3S, packets analyzer, and packet constructor to design and develop the algorithm. Results have shown that the algorithm was capable of detecting and preventing IP spoofed packets on LAN before L3S forward them to a target, and eventually an attacker was identified by using his/her MAC address.
Analysis of New Technology Trends in Education: 2010–2015

This paper analyzes the evolution of technology trends in education from 2010 to 2015, using as input the predictions made in the Horizon Reports on Higher Education, published yearly since 2004. Each edition attempts to forecast the most promising technologies likely to impact on education along three horizons: the short term (the year of the report), the midterm (the following two years), and the long term (the following four years). This paper applies social analysis, based on Google Trends, and bibliometric analysis, with data from Google Scholar and Web of Science, to these predictions in order to discover which technologies were successful and really impacted mainstream education, and which ones failed to have the predicted impact and why. This paper offers guidelines that may be helpful to those seeking to invest in new research areas.
Exploring the Urban Region-of-Interest through the Analysis of Online Map Search Queries

Urban Region-of-Interest (ROI) refers to the integrated urban areas with specific functionalities that attract people's attentions and activities, such as the recreational business districts, transportation hubs, and city landmarks. Indeed, at the macro level, ROI is one of the representatives for agglomeration economies, and plays an important role in urban business planning. At the micro level, ROI provides a useful venue for understanding the urban lives, demands and mobilities of people. However, due to the vague and diversified nature of ROI, it still lacks of quantitative ways to investigate ROIs in a holistic manner. To this end, in this paper we propose a systematic study on ROI analysis through mining the large-scale online map query logs, which provides a new data-driven research paradigm for ROI detection and profiling. Specifically, we first divide the urban area into small region grids, and calculate their PageRank value as visiting popularity based on the transition information extracted from map queries. Then, we propose a density-based clustering method for merging neighboring region grids with high popularity into integrated ROIs. After that, to further explore the profiles of different ROIs, we develop a spatial-temporal latent factor model URPTM (Urban Roi Profiling Topic Model) to identify the latent travel patterns and Point-of-Interest (POI) demands of ROI visitors. Finally, we implement extensive experiments to empirically evaluate our approaches based on the large-scale real-world data collected from Beijing. Indeed, by visualizing the results obtained from URPTM, we can successfully obtain many meaningful travel patterns and interesting discoveries on urban lives.
Reinforcement Learning Based Power Control for In-Body Sensors in WBANs Against Jamming

Wireless body area networks (WBANs) have to address jamming attacks to support health-care applications. In this paper, we present a reinforcement learning-based power control scheme for the communication between the in-body sensors and the WBAN coordinator to resist jamming attacks. This scheme applies Q-learning to guide the coordinator to achieve an optimal power control strategy without being aware of the in-body sensor’s transmission parameters and the WBAN model of the other sensors in the dynamic anti-jamming transmission. In addition, a transfer learning method is adopted to accelerate the learning speed. Stackelberg equilibria and their existence conditions are deduced in a single time slot to upper bound the performance of the learning-based sensor power control scheme. Simulation results show that the proposed scheme can efficiently increase the utilities and decrease the transmission energy consumptions for the in-body sensors and the WBAN coordinator, and simultaneously reduce the attack possibility of the jammer compared with a standard Q-learning-based sensor power control scheme.
Cross-border e-Government Services in Europe: Expected Benefits, Barriers and Drivers of the Once-Only Principle

Moving towards the cross-border mobility of e-Government services is a political priority of the European Union, but the provision of such services remains fragmented. This paper builds on the first research results of "The Once-Only Principle Project" (TOOP), launched in 2017 by the European Commission and 51 organizations from 21 European countries to advance cross-border e-services through exploring and testing the cross-border application of the once-only principle (OOP). According to this principle, public administrations should collect information from citizens and businesses only once and then share this information with other administrative bodies. On the basis of a literature review, survey, focus group interviews and workshops, this paper presents a preliminary take on the benefits associated with cross-border OOP, as well as the barriers and drivers that affect the emergence of OOP-based cross-border e-Government services.