Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing effects of non‐native crayfish on mosquito survival

Introductions of non‐native predators often reduce biodiversity and affect natural predator–prey relationships and may increase the abundance of potential disease vectors (e.g., mosquitoes) indirectly through competition or predation cascades. The Santa Monica Mountains (California, U.S.A.), situated in a global biodiversity hotspot, is an area of conservation concern due to climate change, urbanization, and the introduction of non‐native species. We examined the effect of non‐native crayfish (Procambarus clarkii) on an existing native predator, dragonfly nymphs (Aeshna sp.), and their mosquito larvae (Anopheles sp.) prey. We used laboratory experiments to compare the predation efficiency of both predators, separately and together, and field data on counts of dragonfly nymphs and mosquito larvae sampled from 13 local streams. We predicted a lower predation efficiency of crayfish compared with native dragonfly nymphs and a reduced predation efficiency of dragonfly nymphs in the presence of crayfish. Dragonfly nymphs were an order of magnitude more efficient predators than crayfish, and dragonfly nymph predation efficiency was reduced in the presence of crayfish. Field count data showed that populations of dragonfly nymphs and mosquito larvae were strongly correlated with crayfish presence in streams, such that sites with crayfish tended to have fewer dragonfly nymphs and more mosquito larvae. Under natural conditions, it is likely that crayfish reduce the abundance of dragonfly nymphs and their predation efficiency and thereby, directly and indirectly, lead to higher mosquito populations and a loss of ecosystem services related to disease vector control.
Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy

Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can protect either items or ratings only. In this paper, we propose a recommender system that protects both user's items and ratings. For this, we develop novel matrix factorization algorithms under local differential privacy (LDP). In a recommender system with LDP, individual users randomize their data themselves to satisfy differential privacy and send the perturbed data to the recommender. Then, the recommender computes aggregates of the perturbed data. This framework ensures that both user's items and ratings remain private from the recommender. However, applying LDP to matrix factorization typically raises utility issues with i) high dimensionality due to a large number of items and ii) iterative estimation algorithms. To tackle these technical challenges, we adopt dimensionality reduction technique and a novel binary mechanism based on sampling. We additionally introduce a factor that stabilizes the perturbed gradients. With MovieLens and LibimSeTi datasets, we evaluate recommendation accuracy of our recommender system and demonstrate that our algorithm performs better than the existing differentially private gradient descent algorithm for matrix factorization under stronger privacy requirements.
Distributed Power Allocation Based on LQG Regulator With Adaptive Weight and Switching Scheme for Cognitive Radio Networks

We present a distributed power allocation algorithm for a cognitive radio network (CRN), where the underlying secondary users (SUs) share the same licensed spectrum with the primary users (PUs). Based on the target signal-to-interference-plus-noise ratio (target-SINR) tracking power control (TPC) algorithm in a conventional network, the power allocation problem is modeled as a state-space model with exogenous input that includes varying channel state information (CSI) and some parameter measurement errors. This power allocation algorithm is actually a state feedback controller from the linear quadratic Gaussian (LQG) solution, which is used to guarantee the SINR requirement of the SUs and keep the interference temperature (IT) constraint of all PUs under a given threshold. Considering the quality of service (QoS) of the SU, an adaptive control weight and a switching safety margin of the IT threshold are introduced in this algorithm to achieve better performance. Analysis and simulation results show the effectiveness and advantages of the proposed power allocation scheme, designed based on control theory, compared with those of other traditional algorithms designed based on optimization.
Time-driven activity-based cost of outpatient total hip and knee arthroplasty in different set-ups

Background and purpose — Length of stay (LOS) following total hip and knee arthroplasty (THA and TKA) has been reduced over the years due to fast-track. Short stays of 2 days in fast-track departments in Denmark have resulted in low total costs of around US$2,550. Outpatient THA and TKA is gaining popularity, albeit in a limited and selected group of patients; however, the financial benefit of outpatient arthroplasty remains unknown. We present baseline detailed economic calculations of outpatient THA and TKA in 2 different settings: one from the hospital and another from the ambulatory surgery department. Patients and methods — Data from 6 patients (1 TKA, 1 uncemented THA, 1 cemented THA in each department) were collected prospectively using the Time Driven Activity Based Costing method (TDABC). Time consumed by different staff members involved in patient treatment in the perioperative period of outpatient THA and TKA was calculated in 2 different settings: one in the orthopedic department and one in the ambulatory surgery department. Results — LOS was around 11 h in the orthopedic department and around 7 h in the ambulatory surgery department, respectively. TDABC revealed minor differences in the operative settings between departments and similar expenses occurred during the short stay of US$777 and US$746, respectively. Adding the preoperative preparation and postoperative follow-up resulted in total cost of US$951 and US$942 for the ward and the ambulatory surgery department, respectively. Interpretation — Outpatient THA and TKA in hospital and ambulatory surgery departments results in similar cost using the TDABC method. Compared with the cost associated with 2-day stays, outpatient procedures are around two-thirds cheaper provided no increase occurs in complications or readmissions.
Spiral^SRA: A Threat-Specific Security Risk Assessment Framework for the Cloud

Conventional security risk assessment approaches for cloud infrastructures do not explicitly consider risk with respect to specific threats. This is a challenge for a cloud provider because it may apply the same risk assessment approach in assessing the risk of all of its clients. In practice, the threats faced by each client may vary depending on their security requirements. The cloud provider may also apply generic mitigation strategies that are not guaranteed to be effective in thwarting specific threats for different clients. This paper proposes a threat-specific risk assessment framework which evaluates the risk with respect to specific threats by considering only those threats that are relevant to a particular cloud client. The risk assessment process is divided into three phases which have inter-related activities arranged in a spiral. Application of the framework to a cloud deployment case study shows that considering risk with respect to specific threats leads to a more accurate quantification of security risk. Although our framework is motivated by risk assessment challenges in the cloud it can be applied in any network environment.
An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network

Unlike single geospatial objects extraction from high-resolution remote sensing images, the task of road extraction faces more challenges, including its narrowness, sparsity, diversity, multiscale characteristics, and class imbalance. Focusing on these challenges, this paper proposes an end-to-end framework called the multiple feature pyramid network (MFPN). In MFPN, we design an effective feature pyramid and a tailored pyramid pooling module, taking advantage of multilevel semantic features of high-resolution remote sensing images. In the optimization stage, a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads. The proposed novel loss function is more sensitive to the misclassified and the sparse real labeled pixels and helps to focus on the spare set of hard pixels in the training stage. Compared with the cross-entropy loss function, the weighted balance loss can reduce training time dramatically for the same precision. Experiments on two challenging datasets of high-resolution remote sensing images which illustrate the performance of the proposed algorithm have achieved significant improvements, especially for narrow rural roads.
Comparative Effectiveness of Neoadjuvant Treatments for Resectable Gastroesophageal Cancer: A Network Meta-Analysis

Background: Several neoadjuvant treatments are available for patients with resectable gastroesophageal cancer. We did a Bayesian network meta-analysis (NMA) to compare available treatments, summarizing the direct and indirect evidence. Method: We searched relevant databases for randomized controlled trials of neoadjuvant treatments for resectable gastroesophageal cancer which compared two or more of the following treatments: surgery alone, perioperative docetaxel, oxaliplatin, leucovorin, and fluorouracil (FLOT), and neoadjuvant treatments listed in National Comprehensive Cancer Network guideline. Then we performed a NMA to summarize the direct and indirect evidence to estimate the relative efficacy for outcomes including overall survival (OS), progression-free survival and R0 resection rate. We calculated odds ratio (OR) and hazard ratio (HR) with 95% credible intervals (CrI) for dichotomous data and time-to-event data, respectively. We also calculated the surface under the cumulative ranking curve (SUCRA) value of each intervention to obtain a hierarchy of treatments. Result: Eight eligible trials (2434 patients) were included in our NMA. The treatment with the highest probability of benefit on OS as compared with surgery alone was perioperative FLOT [HR = 0.58 with 95% CrI: (0.43, 0.78), SUCRA = 93%], followed by preoperative radiotherapy, paclitaxel, and carboplatin (RT/PC) [HR = 0.68 with 95% CrI: (0.53, 0.87), SUCRA = 72%], perioperative cisplatin with fluorouracil (CF) [HR = 0.70 with 95% CrI: (0.51, 0.95), SUCRA = 68%], and perioperative epirubicin, cisplatin, and fluorouracil or capecitabine (ECF/ECX) [HR = 0.75 with 95% CrI: (0.60, 0.94), SUCRA = 56%]. Conclusion: Compared with surgery alone, perioperative CF, perioperative ECF/ECX, perioperative FLOT, and preoperative RT/PC significantly improved survival. Perioperative FLOT is likely to be the most effective neoadjuvant treatment for the disease. Further clinical studies are needed and justified.
Debugging Multithreaded Programs as if They Were Sequential

Debugging multithread programs is extremely difficult because the basic assumption that underlies sequential program debugging, that is, the program behavior is deterministic under a fixed input, is no longer valid due to the nondeterminism attributed to thread scheduling. It is because the programs behavior is non-deterministic due to the nondeterminism of parallel execution, which makes debugging or testing multithreaded programs extremely difficult. In this paper, we propose a proactive debugging method to restore this basic assumption so that programmers can debug multithreaded programs as if they were sequential. Our approach is based on the synergistic integration of symbolic analysis and dynamic analysis techniques. In particular, symbolic analysis investigates the program behavior under the same input to search whether there is a thread schedule would trigger a bug or a not-yet-explored path. Dynamic analysis is to execute these new paths with the guide of the generated thread schedules, thereby further guiding the symbolic analysis. The net effect of applying this feedback loop is a systematic and complete coverage of the program behavior under a fixed test input. We implement the proposed approach in a prototype tool called proactive-debugger. Our experiments show that proactive-debugger outperforms both ESBMC and Maple, which are two powerful and well-known testing tools for failure detection in multithreaded programs.
AFM Laser Texturing on Chitosan/Au Precursor nanocomposite Materials for Lithography Technique

Innovative conductive nanocomposite materials (NCM) could represent an important evolution for sensors and electronics applications. The design and processing of conductive elements as electrodes directly in a polymeric bulk material can be considered as an interesting improvement. By this way, the use of an Atomic Force Microscope (AFM) instrument with its accurate and precise motorized table and its implemented laser can be used for designing conductive elements in polymeric materials. In that case, 805 nm infrared (IR) laser light from the AFM instrument inducing surface texturing on Chitosan/Au Precursor (C/AuP) substrate is reported. Surface patterns consisting of designed structures were produced by laser texturing. By fine-tuning key parameters as the programmed movement of the motorized table supporting the substrate in accordance to the laser movement as well as the working distance, Au nanoparticles (AuNP) were locally generated. Micro patterns have been generated on the nanocomposite C/AuP surface. By the use of NCM able to generate in-situ nanoparticles (NP) under light stimulation, micro structures can be generated on the NCM surfaces, preserving their flexibility and easy processability. Control of conductive properties is desirable in a wide range of domains as medicine, health care and industry in particular, especially, if the material properties can be modified by a simple technique.
Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study

The goal of data analytics is to delineate hidden patterns and use them to support informed decisions in a variety of situations. Credit card fraud is escalating significantly with the advancement of the modernized technology and become an easy target for fraudulent. Credit card fraud is a severe problem in the financial service and costs billions of a dollar every year. The design of fraud detection algorithm is a challenging task with the lack of real-world transaction dataset because of confidentiality and the highly imbalanced publicly available datasets. In this paper, we apply different supervised machine learning algorithms to detect credit card fraudulent transaction using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms exist in literature against the super classifier that we implemented in this paper.
Zero-Shot Learning via Low-Rank-Representation Based Manifold Regularization

In this letter, we propose a novel low-rank-represen-tation (LRR) based manifold-regularization approach for zero-shot learning (ZSL). Most existing regularization-based ZSL approaches perform the alignment between visual feature space and semantic space based on the affinity matrix constructed from the test instances. The affinity matrix plays a significant role in exploiting the manifold structures of visual feature space, hence we propose to use the LRR to guide the affinity-matrix construction by exploring the subspace structures of data. Considering the locality and similarity information among data, we incorporate a Laplacian regularization term to the LRR framework to ensure that the learned affinity matrix can capture the local geometric structures in data. We also explicitly impose the nonnegative sparse constraint on the affinity matrix to facilitate the learning of local manifold structures. Moreover, we use an effective manifold-regularization methodology to learn discriminative semantic representations of test instances, leading to significant improvements in classification performance over the unseen classes. Extensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms the state of the arts.
Influences of salinity on the physiology and distribution of the Arctic coralline algae, Lithothamnion glaciale (Corallinales, Rhodophyta)

In Greenland, free‐living red coralline algae contribute to and dominate marine habitats along the coastline. Lithothamnion glaciale dominates coralline algae beds in many regions of the Arctic, but never in Godthåbsfjord, Greenland, where Clathromorphum sp. is dominant. To investigate environmental impacts on coralline algae distribution, calcification and primary productivity were measured in situ during summers of 2015 and 2016, and annual patterns of productivity in L. glaciale were monitored in laboratory‐based mesocosm experiments where temperature and salinity were manipulated to mimic high glacial melt. The results of field and cold‐room measurements indicate that both L. glaciale and Clathromorphum sp. had low calcification and photosynthetic rates during the Greenland summer (2015 and 2016), with maximum of 1.225 ± 0.17 or 0.002 ± 0.023 μmol CaCO3 · g−1 · h−1 and −0.007 ±0.003 or −0.004 ± 0.001 mg O2 · L−1 · h−1 in each species respectively. Mesocosm experiments indicate L. glaciale is a seasonal responder; photosynthetic and calcification rates increase with annual light cycles. Furthermore, metabolic processes in L. glaciale were negatively influenced by low salinity; positive growth rates only occurred in marine treatments where individuals accumulated an average of 1.85 ± 1.73 mg · d−1 of biomass through summer. These results indicate high freshwater input to the Godthåbsfjord region may drive the low abundance of L. glaciale, and could decrease species distribution as climate change increases freshwater input to the Arctic marine system via enhanced ice sheet runoff and glacier calving.
iSPA-Net: Iterative Semantic Pose Alignment Network

Understanding and extracting 3D information of objects from monocular 2D images is a fundamental problem in computer vision. In the task of 3D object pose estimation, recent data driven deep neural network based approaches suffer from scarcity of real images with 3D keypoint and pose annotations. Drawing inspiration from human cognition, where the annotators use a 3D CAD model as structural reference to acquire ground-truth viewpoints for real images; we propose an iterative Semantic Pose Alignment Network, called iSPA-Net. Our approach focuses on exploiting semantic 3D structural regularity to solve the task of fine-grained pose estimation by predicting viewpoint difference between a given pair of images. Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation. The fine-grained object pose estimator is also aided by correspondence of learned spatial descriptor of the input image pair. The proposed pose alignment framework enjoys the faculty to refine its initial pose estimation in consecutive iterations by utilizing an online rendering setup along with effectiveness of a non-uniform bin classification of pose-difference. This enables iSPA-Net to achieve state-of-the-art performance on various real image viewpoint estimation datasets. Further, we demonstrate effectiveness of the approach for multiple applications. First, we show results for active object viewpoint localization to capture images from similar pose considering only a single image as pose reference. Second, we demonstrate the ability of the learned semantic correspondence to perform unsupervised part-segmentation transfer using only a single part-annotated 3D template model per object class. To encourage reproducible research, we have released the codes for our proposed algorithm.
Cross-Entropy: A New Metric for Software Defect Prediction

Defect prediction is an active topic in software quality assurance, which can help developers find potential bugs and make better use of resources. To improve prediction performance, this paper introduces cross-entropy, one common measure for natural language, as a new code metric into defect prediction tasks and proposes a framework called DefectLearner for this process. We first build a recurrent neural network language model to learn regularities in source code from software repository. Based on the trained model, the cross-entropy of each component can be calculated. To evaluate the discrimination for defect-proneness, cross-entropy is compared with 20 widely used metrics on 12 open-source projects. The experimental results show that cross-entropy metric is more discriminative than 50% of the traditional metrics. Besides, we combine cross-entropy with traditional metric suites together for accurate defect prediction. With cross-entropy added, the performance of prediction models is improved by an average of 2.8% in F1-score.
Efficient Coastal Communications with Sparse Network Coding

The demand for wideband communication in the coastal area (i.e., ≤ 100 km from the coastline) has been rapidly increasing in recent years. Compared to the terrestrial scenario, the coastal environment has long-distance and highly dynamic channels, and the communication devices are more strictly constrained by energy supplies. While the RLNC has the fountain erasure-correction property and is suitable for transmissions over the long-distance dynamic channels, it suffers from high coding coefficient delivery cost and decoding complexity. In this article, we look into the application of sparse network coding in coastal communication systems. We identify two typical multicast scenarios that may appear in coastal communications, namely the relay-aided multicast and multicast from a shore-based base station with D2D communication enabled among the subscribers. We provide a detailed comparison of existing sparse network coding schemes. Based on that, we demonstrate through simulations that an appropriate choice of sparse codes is critical to meet the unique requirements in coastal communication systems. We show that batched sparse code is suitable for relay-aided multicast, and subset-based sparse codes are preferable for D2D-enabled multicast.
Comparative Analysis of Energy-Efficient Scheduling Algorithms for Big Data Applications

Nowadays, big data analytics has been widely applied in addressing the growing cybercrime threats. However, energy consumption is explosive increasing with the fast growth of big data processing in anti-cybercrime. In this paper, an energy-efficient framework for big data applications is proposed to reduce energy consumption while satisfying deadline constrains. First, the problem of energy-efficient tasks scheduling of a single Spark job is modeled as an integer program. We design an energy-efficient tasks scheduling algorithm to minimize the energy consumption for big data application in Spark. To avoid service-level agreement violations for execution time, we propose an optimal task scheduling algorithm with deadline constrains by tradingoff execution time and energy consumption. Experiments on a Spark cluster are performed to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite. Our algorithms consume less energy on average than FIFO and FAIR under deadlines. The optimal algorithm is able to find near optimal tasks schedules to trade off energy consumed and response time benefit in small shuffle partitions.