Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Based Design of Energy-Efficient Applications for IoT Systems

A major challenge that is currently faced in the design of applications for the Internet of Things (IoT) concerns with the optimal use of available energy resources given the battery lifetime of the IoT devices. The challenge is derived from the heterogeneity of the devices, in terms of their hardware and the provided functionalities (e.g data processing/communication). In this paper, we propose a novel method for (i) characterizing the parameters that influence energy consumption and (ii) validating the energy consumption of IoT devices against the system's energy-efficiency requirements (e.g. lifetime). Our approach is based on energy-aware models of the IoT application's design in the BIP (Behavior, Interaction, Priority) component framework. This allows for a detailed formal representation of the system's behavior and its subsequent validation, thus providing feedback for enhancements in the pre-deployment or pre-production stages. We illustrate our approach through a Building Management System, using well-known IoT devices running the Contiki OS that communicate by diverse IoT protocols (e.g. CoAP, MQTT). The results allow to derive tight bounds for the energy consumption in various device functionalities, as well as to validate lifetime requirements through Statistical Model Checking.
Multi-level per node combiner (MLPNC) to minimize mapreduce job latency on virtualized environment

Big data drove businesses and researches more data driven. Hadoop MapReduce is one of the cost-effective ways for processing huge amount of data and also offered as a service from cloud on cluster of Virtual Machines (VM). In Cloud Data Center (CDC), Hadoop VMs are co-located with other general purpose VMs across racks. Such a multi-tenancy leads to varying local network bandwidth availability for Hadoop VMs, which directly impacts MapReduce job latency. Because, shuffle phase in MapReduce execution sequence itself contributes 26%-70% of overall job latency due to large number of intermediate records. Therefore, Hadoop virtual cluster requires to ensure a maximum bandwidth to minimize job latency, but, it also increases the bandwidth usage cost. In this paper, we propose "Multi-Level Per Node Combiner" (MLPNC) that curtails the number of intermediate records in shuffle phase resulting to reduction in overall job latency. It also minimizes bandwidth usage cost as well. We evaluate MLPNC results on wordcount job against default combiner, and Per Node Combiner (PNC). We also discuss the results based on number of shuffled records, shuffle latency, average merge latency, average reduce latency, average reduce task start time, and overall job latency. Finally, we argue in favor of MLPNC as it achieves up to 33% reduction in number of intermediate records and up to 32% reduction in average job latency than PNC.
On the structure and authorization management of RESTful web services

A broad range of emerging business models relies on the continual exchange of data that flow among different services to generate additional value and derive knowledge in many domains. The magnitude of resource sharing that form the basis of these interactions raises new challenges concerning the effectivity of existing security and privacy management instruments in environments of such complexity. In this work, we examine the practical application of authorization management mechanisms employed over RESTful Web APIs, which today serve as a major approach to expose service interfaces on the web. For this purpose, we have examined the integration of security mechanisms in n=523 public Web APIs. Our findings reveal alarming integration patterns that demonstrate a rudimentary data security and privacy protection in cross-service resource sharing. Our analysis traces the cause back to the (1) shallow models and security capabilities offered by service providers, and (2) design deficiencies of dominantly applied OAuth 2.0 web authorization framework that restrict capabilities and lower the interoperability of underlying management functions. Following the initial discussion, we summarize potential solutions and establish an outline of the future work.
An integer linear-programming based resource allocation method for SQL-like queries in the cloud

Cloud computing has emerged as a paradigm for delivering Information Technology services over Internet. Services are provided according to a pricing model and meet requirements that are specified in Service Level Agreements (SLA). Recently, most of cloud providers include services for DataBase (DB) querying dedicated to run on MapReduce platform and a virtualized architecture. Classical resource allocation methods for query optimization need to be revised to handle the pricing models in cloud environnements. In this work, we propose a resource allocation method for the query optimization in the cloud based on Integer Linear-Programming (ILP). The proposed linear models can be implemented in any fast solver for ILP. The method is compared with some existing greedy algorithms. Experimental evaluation shows that the solution offers a good trade-off between the allocation quality and allocation cost.
The Pseudomonas Quinolone Signal (PQS): Not Just for Quorum Sensing Anymore

The Pseudomonas quinolone signal (PQS) has been studied primarily in the context of its role as a quorum-sensing signaling molecule. Recent data suggest, however, that this molecule may also function to mediate iron acquisition, cytotoxicity, outer-membrane vesicle biogenesis, or to exert host immune modulatory activities.
Single Cell RNA Sequencing of Rare Immune Cell Populations

Single-cell RNA sequencing (scRNA-Seq) is transforming our ability to characterize cells, particularly rare cells that are often overlooked in bulk population analytical approaches. This has lead to the discovery of new cell types and cellular states that echo the underlying heterogeneity and plasticity in the immune system. Technologies for the capture, sequencing, and bioinformatic analysis of single cells are rapidly improving, and scRNA-Seq is now becoming much more accessible to non-specialized laboratories. Here, we describe our experiences in adopting scRNA-Seq to the study of rare immune cells in their microanatomical niches.
Functional Dissection of Auxin Response Factors in Regulating Tomato Leaf Shape Development

The phytohormone auxin is involved in many aspects of plant growth and developmental processes. The tomato Aux/IAA transcription factor SlIAA9/ENTIRE/E plays an important role in leaf morphogenesis and fruit development, and the E gene encodes a protein from the Aux/IAA family of auxin response repressors. Both SlIAA9-RNAi transgenic and entire (e) mutant plants reduce the leaf complexity in tomato, but the underlying mechanism is not yet completely resolved. Auxin signaling is known to regulate target genes expression via Aux/IAA and ARFs (auxin response factors) transcriptional regulators. ARFs mediate a wide range of developmental processes. Through an Y2H (yeast two-hybrid) assay coupled with expression profiling of the SlARF genes family, we identified a group of ARFs: SlARF6A, SlARF8A, SlARF8B, and SlARF24. Pull-down and BiFC (Bimolecular Fluorescence Complementation) results demonstrated that these SlARFs interact with SlIAA9 in vitro and in vivo, and the e mutation altered the expression patterns of multiple SlARFs. The simple leaves of the e mutant were partially converted to wild-type compound leaves by VIGS (virus-induced gene silencing) of these four SlARFs. Furthermore, IAA content in these samples was significantly increased compared to the e mutant. In addition, SlARF6A and SlARF24 bound to the SlPIN1 promoter and act as transcriptional activators to regulate genes expression involved in leaflet initiation. It may also suggest that SlARFs regulate leaf morphology through direct binding to auxin-responsive genes in the absence of SlIAA9, providing an insight for the role of SlARFs in leaf shape development.
A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure

Fog computing is a dense, heterogeneous and geographically distributed infrastructure. With the rise of IoT applications, objects may generate large amounts of data that may be processed at different locations of the Fog infrastructure. Data placement strategies have been designed to investigate the best storage location for data in order to reduce its access time for different IoT services spread over the infrastructure. Unfortunately, due to the large number of Fog nodes and the amount of data to be managed, placing data in such infrastructure is an NP-Hard problem. In this paper, we propose a divide and conquer heuristic for data placement strategies in Fog infrastructures. Our idea consists in dividing the original data placement problem into several balanced sub-problems using graph modeling and partitioning methods. Using our heuristic makes it possible to reduce the solving time by more than 450 times with less than 5% of optimality loss as compared to the exact solution (without subdivision). For a given number of partitions, our solution proved to be at least as close to the optimal as state-of-the-art solutions and 30% closer to the optimal for many workloads. In addition, our solution allowed for a better optimization in solving time as it is more flexible and scalable in terms of number of partitions.
Towards Software-Defined and Self-Driving Cloud Infrastructure

Traditionally, people abstract away the infrastructure operation, such as power management, network traffic engineering and even the "cloud computing"? layers from software developers. This abstraction brings easier application development and maintenance but leads to complexities and inefficiencies for infrastructure operation. In this talk, we present our recent research into this area. Using data center power management, networking as well as cloud software management as examples.
An empirical study on the effects of industrial clusters on small and medium enterprises

Article history: Received March 26, 2012 Accepted 15 June 2012 Available online June 16 2012 Tendency to industrial clustering at the end of the twentieth century was a turning point in industrial development programs in most countries of the world. Industrial clustering is a model of industrial organization, which has entered industrial literature as a new development strategy. Clustering has provided utilization of efficiencies of scale and aggregation as well as efficiencies of collective efforts by creating conditions for competitiveness advantage, economical growth and export development in the international environment. Today, industrial clustering has been considered as a fundamental strategy for economic development and growth in almost all countries of the world including Iran. This paper aims to assess and to explain important factors affecting the competitiveness of industrial clusters. It also shows the relationship among these factors together. The proposed study uses fuzzy DEMATEL technique; hence, the affecting factors of competitiveness of industrial clusters in the automotive cluster in one of the provinces of Iran is investigated and the importance of each of these factors and their relationships are identified. © 2012 Growing Science Ltd. All rights reserved.
Temperate and Tropical Forest Canopies are Already Functioning beyond Their Thermal Thresholds for Photosynthesis

Tropical tree species have evolved under very narrow temperature ranges compared to temperate forest species. Studies suggest that tropical trees may be more vulnerable to continued warming compared to temperate species, as tropical trees have shown declines in growth and photosynthesis at elevated temperatures. However, regional and global vegetation models lack the data needed to accurately represent such physiological responses to increased temperatures, especially for tropical forests. To address this need, we compared instantaneous photosynthetic temperature responses of mature canopy foliage, leaf temperatures, and air temperatures across vertical canopy gradients in three forest types: tropical wet, tropical moist, and temperate deciduous. Temperatures at which maximum photosynthesis occurred were greater in the tropical forests canopies than the temperate canopy (30 ± 0.3 °C vs. 27 ± 0.4 °C). However, contrary to expectations that tropical species would be functioning closer to threshold temperatures, photosynthetic temperature optima was exceeded by maximum daily leaf temperatures, resulting in sub-optimal rates of carbon assimilation for much of the day, especially in upper canopy foliage (>10 m). If trees are unable to thermally acclimate to projected elevated temperatures, these forests may shift from net carbon sinks to sources, with potentially dire implications to climate feedbacks and forest community composition.
AUDIO-MATERIAL MODELING AND RECONSTRUCTION FOR MULTIMODAL INTERACTION

Charles Auston Baker Sterling: Audio-Material Modeling and Reconstruction for Multimodal Interaction (Under the direction of Ming C. Lin) Interactive virtual environments enable the creation of training simulations, games, and social applications. These virtual environments can create a sense of presence in the environment: a sensation that its user is truly in another location. To maintain presence, interactions with virtual objects should engage multiple senses. Furthermore, multisensory input should be consistent, e.g., a virtual bowl that visually appears plastic should also sound like plastic when dropped on the floor. In this dissertation, I propose methods to improve the perceptual realism of virtual object impact sounds and ensure cohesion between those sounds and the input from other senses. Recreating the impact sound of a real-world object requires an accurate estimate of that object’s material parameters. The material parameters that affect impact sound—collectively forming the audio-material—include the material damping parameters for a damping model. I propose and evaluate damping models and use them to estimate material damping parameters for real-world objects. I also consider how interaction with virtual objects can be made more cohesive between the senses of sight, hearing, and touch. First, I present a method for modeling the damping behavior of impact sounds using generalized proportional damping. Generalized proportional damping is applied to both perform impact sound synthesis and estimate more expressive material damping parameters from recorded impact sounds. Next, I present a method for estimating material damping parameters in the presence of confounding factors and with no knowledge of the object’s shape. To accomplish this, a probabilistic damping model captures various external effects to produce robust damping parameter estimates. Next, I present a method for cohesive multimodal interaction with textured surfaces. Texture maps serve as a single unified representation of mesoscopic detail for the purposes of visual rendering, sound synthesis, and rigid-body simulation. Finally, I present a method for geometry and material classification using multimodal audio-visual input. Using this method, a real-world scene can be scanned and virtually reconstructed while accurately modeling both the visual appearances and audio-material parameters of each object.
Evaluation of morphological hierarchies for supervised video segmentation

This paper aims to evaluate the morphological hierarchies (observation scale, watersheds area and volume) in the context of marked video segmentation. Here, we automatically created a set of markers for each video taking into account its ground-truth. In order to evaluate the hierarchies, we have applied several types of markers and their combinations for simulating supervised segmentation idea. The performance of each segmentation was evaluated in terms of F-measures, and according to our results, the observation scale hierarchies outperform the considered hierarchies when the markers for both background and foreground were skeleton-based.
GAC: graph-based alert correlation for the detection of distributed multi-step attacks

Monitoring tools like Intrusion Detection Systems (IDS), Firewalls, or Honeypots are a second line of defense in the face of an increasing number of distributed, increasingly sophisticated, and targeted attacks. A huge amount of security alerts needs to be analysed and correlated to gather the complete picture of an attack. However, most conventional IDS fall short in correlating alerts that have different sources, so that many distributed attacks remain completely unnoticed. In this paper, we propose Graph-based Alert Correlation (GAC), a novel correlation algorithm that isolates attacks, identifies attack scenarios, and assembles multi-stage attacks from huge alert sets. Our evaluation results on artificial and real-world data indicates that GAC is robust against false positives, can detect distributed attacks, and scales with an increasing number of alerts.
Model-based Hand Pose Estimation for Generalized Hand Shape with Appearance Normalization

Since the emergence of large annotated datasets, state-of-the-art hand pose estimation methods have been mostly based on discriminative learning. Recently, a hybrid approach has embedded a kinematic layer into the deep learning structure in such a way that the pose estimates obey the physical constraints of human hand kinematics. However, the existing approach relies on a single person's hand shape parameters, which are fixed constants. Therefore, the existing hybrid method has problems to generalize to new, unseen hands. In this work, we extend the kinematic layer to make the hand shape parameters learnable. In this way, the learnt network can generalize towards arbitrary hand shapes. Furthermore, inspired by the idea of Spatial Transformer Networks, we apply a cascade of appearance normalization networks to decrease the variance in the input data. The input images are shifted, rotated, and globally scaled to a similar appearance. The effectiveness and limitations of our proposed approach are extensively evaluated on the Hands 2017 challenge dataset and the NYU dataset.
Adaptive Sensitivity Analysis with Nonlinear Power Load Modeling

Voltage fluctuation in power networks is a critical issue for VLSI designs. The analysis and optimization of the voltage drops rely on accurate sensitivity calculation. Due to the high complexity of large-scale circuits, in practice active devices are simplified with power load models. In order to grasp the details of voltage fluctuation, we propose a nonlinear power load model as a function of the supply voltage. We adopt an adaptive analysis algorithm to accelerate the calculation. The power load model provides efficient and accurate noise prediction at system-level.
Automatic scalable parallel test case execution. introducing the münster distributed test case runner for Java (miDSTR)

Software testing is a broad research field and of great relevance to practitioners. Software testing involves multiple consecutive testing phases. One of these phases is the is the unit-testing phase, during which individual requirements of the software component are checked. Only once software-units and -components have been tested, can the testing process continue. Trivially, if this phase requires a great amount time, the testing process is delayed and in the worst case, the rollout of the system has to be rescheduled. Focusing on the widely adopted JUnit test framework, we propose a system to execute test cases on multiple distributed virtual machines. This paper presents an approach to connect multiple Java Virtual Machines (JVMs) into a test-net, which can execute a JUnit test suite in parallel. Furthermore, different distribution schemata are discussed. By distributing the execution to multiple VMs, the overall time to test is reduced, in order to improve the overall time required for the testing process.
Query Expansion as a Matrix Factorization Problem: Extended Abstract

Pseudo-relevance feedback (PRF) provides an automatic method for query expansion in Information Retrieval. These techniques find relevant expansion terms using the top retrieved documents with the original query. In this paper, we present an approach based on linear methods called LiMe that formulates the PRF task as a matrix factorization problem. LiMe learns an inter-term similarity matrix from the pseudo-relevant set and the query that uses for computing expansion terms. The experiments on five datasets show that LiMe outperforms state-of-the-art baselines in most cases.
Uniform generation of spanning regular subgraphs of a dense graph

Let $H_n$ be a graph on $n$ vertices and let $\overline{H_n}$ denote the complement of $H_n$. Suppose that $\Delta = \Delta(n)$ is the maximum degree of $\overline{H_n}$. We analyse three algorithms for sampling $d$-regular subgraphs ($d$-factors) of $H_n$. This is equivalent to uniformly sampling $d$-regular graphs which avoid a set $E(\overline{H_n})$ of forbidden edges. Here $d=d(n)$ is a positive integer which may depend on $n$. Two of these algorithms produce a uniformly random $d$-factor of $H_n$ in expected runtime which is linear in $n$ and low-degree polynomial in $d$ and $\Delta$. The first algorithm applies when $(d+\Delta)d\Delta = o(n)$. This improves on an earlier algorithm by the first author, which required constant $d$ and at most a linear number of edges in $\overline{H_n}$. The second algorithm applies when $H_n$ is regular and $d^2+\Delta^2 = o(n)$, adapting an approach developed by the first author together with Wormald. The third algorithm is a simplification of the second, and produces an approximately uniform $d$-factor of $H_n$ in time $O(dn)$. Here the output distribution differs from uniform by $o(1)$ in total variation distance, provided that $d^2+\Delta^2 = o(n)$.
Managing hybrid memories by predicting object write intensity

Emerging Non-Volatile Memory (NVM) technologies offer more capacity and energy efficiency than DRAM, but their write endurance is lower and latency is higher. Hybrid memories seek the best of both worlds — scalability, efficiency, and performance — by combining DRAM and NVM. Our work proposes modifying a standard managed language runtime to allocate objects either in DRAM or NVM to maximize the use of NVM capacity without wearing it out. The key to our approach is correctly predicting highly mutated objects and allocating them in DRAM and allocating rarely mutated objects in NVM. We explore write-intensity prediction based on object (1) size, (2) class type, and (3) allocation site. We find predictions using allocation site are the most accurate. Our memory manager for hybrid memories consists of (1) an offline profiling phase that produces placement advice on a per allocation-site basis, and (2) a garbage collector that allocates mature objects in DRAM or NVM based on this advice and that allocates highly mutated nursery objects in DRAM. We explore two heuristics for classifying sites as write-intensive (DRAM) or rarely written (NVM). Write-Frequency (FREQ) uses the number of writes to objects allocated by each site. Although it can limit writes to NVM up to 1% and 3%, it allocates just 50% to 20% of mature objects in DRAM. Write-Density (DENS) computes number of writes to objects relative to object size. Write-Density is a better predictor. When it limits NVM writes to 2%, it can allocate 88% of mature objects to NVM. Pareto optimal configurations may increase writes to 10% and store 99% of mature objects in NVM. Using write-intensity predictors to proactively place objects in hybrid DRAM and NVM memory systems prolongs NVM’s lifetime while exploiting its capacity.