Miracle-dz/UniFAR-FTUs huggingface dataset:
MBECN: Enabling ECN with Micro-burst Traffic in Multi-queue Data Center

Modern multi-queue data centers often use the standard Explicit Congestion Notification (ECN) scheme to achieve high network performance. However, one substantial drawback of this approach is that micro-burst traffic can cause the instantaneous queue length to exceed the ECN’s threshold, resulting in numerous mismarkings. After enduring too many mismarkings, senders may overreact, leading to severe throughput loss. As a solution to this dilemma, we propose our own adaptationthe Micro-burst ECN (MBECN) scheme-to mitigate mismarking. MBECN finds a more appropriate threshold baseline for each queue to absorb micro-bursts, based on steady-state analysis and an ideal generalized processor sharing (GPS) model. By adopting a queue-occupation-based dynamically adjusting algorithm, MBECN effectively handles packet backlog without hurting latency. Through testbed experiments, we find that MBECN improves throughput by ~20% and reduces flow completion time (FCT) by ~40%. Using large scale simulations, we find that throughput can be improved by 1.5~2.4× with DCTCP and 1.26~1.35× with ECN*. We also measure network delay and find that latency only increases by 7.36%.
Rapid Carbapenemase Detection With Xpert Carba-R V2 Directly On Positive Blood Vials

Abstract The rapid detection of carbapenemase allows implementation of infection control measures and adaptation of antibiotic therapy. We evaluated the performances of the Xpert Carba-R V2® assay for the direct detection and identification of carbapenemase on positive blood cultures. We focused our evaluation on its detection capacity and on the risks of interference due to the patient’s blood. Isolates of several variants of OXA-48-like (n=10), KPC (n=10), NDM (n=11), VIM (n=7), IMP-1 (n=1) carbapenemases and 14 non carbapenemase-producing Enterobacteriaceae were tested. For each isolate (n=53), an aerobic vial was seeded, and incubated in Bactec Fx (Becton Dickinson®) automate. When positive, the Xpert® Carba-R-V2 assay was assessed for carbapenemase detection using 40 µl aliquot. Reproducibility tests were performed on a subset of 23 isolates using aerobic and anaerobic vials. Longer incubation time was also evaluated on 6 isolates. A complementary prospective study in real-time testing of patient-derived clinical samples on 20 additional positive blood vials with Gram negative bacilli on direct examination was performed. Perfect sensitivity and specificity (100%) were observed regardless of the carbapenemase type, the blood vials used and the time of incubation. Xpert® Carba-R-V2 assay is suitable for the rapid detection of the main carbapenemase genes directly on positive blood vials. Its performances and rapid time analysis allow its use in routine to guide therapeutic choices and to implement infection control measures.
Noise Suppression Chanel Estimation Method Using Deep Learning in IEEE 802.11p Standard

In this paper, we propose a channel estimation method based on a complex valued regression of the neural network for the IEEE 802.11p standard. It consists of the complex weighted summation optimized by feedforward neural network with backpropagation algorithm using initial estimated channel of the pilot and the long preamble. It also exploits the shift matrix in order to mitigate the effect from a systemic problems in IEEE 802.11p standards. The major problems of IEEE 802.11p standard are wide bandwidth of 10 MHz consisting of 64 subcarriers and relatively insufficient four pilot subcarriers at single ODFM symbol, which are unsuitable for a channel of vehicular environment. Despite these problems, the proposed method performs better than the conventional channel estimation methods. The performance of proposed scheme is provided with the comparison between constructed data pilots (CDP), Spectral Temporal Averaging (STA), and proposed scheme. The proposed channel estimation scheme has low mean square error (MSE) and bit error rate (BER) throughout the whole SNR region. It is the result from properly trained weight. At the low SNR region, especially, the performance of proposed scheme is much better than CDP and STA scheme. It is because of the noise suppression effect caused by a weighted summation algorithm.
System Architecture for Network-Attached FPGAs in the Cloud using Partial Reconfiguration

Emerging applications such as deep neural networks, bioinformatics or video encoding impose a high computing pressure on the Cloud. Reconfigurable technologies like Field-Programmable Gate Arrays (FPGAs) can handle such compute-intensive workloads in an efficient and performant way. To seamlessly incorporate FPGAs into existing Cloud environments and leverage their full power efficiency, FPGAs should be directly attached to the data center network and operate independent of power-hungry CPUs. This raises new questions about resource management, application deployment and network integrity. We present a system architecture for managing a large number of network-attached FPGAs in an efficient, flexible and scalable way. To ensure the integrity of the infrastructure, we use partial reconfiguration to separate the non-privileged user logic from the privileged system logic. To create a really scalable and agile cloud service, the management of all resources builds on the Representational State Transfer (REST) concept.
DocFigure: A Dataset for Scientific Document Figure Classification

Document figure classification (DFC) is an important stage of a document figure understanding system. The design of a DFC system required a well defined figure categories and dataset. To the best of the author's knowledge, the existing datasets related to classification of figures in the document images are limited with respect to their size and categories [1]–[3]. In this paper, we introduce a scientific figure classification dataset, named as DocFigure. The dataset consists of 33K annotated figures of 28 different categories present in the document images which correspond to scientific articles published in the CVPR, ECCV, ICCV, etc. conferences in last several years. Manual annotation of such a large number (33K) of figures is time consuming and cost ineffective. In this article, we design a web based annotation tool which can efficiently assign category labels to large number of figures with the minimum efforts of human annotators. To benchmark our generated dataset on classification task, we propose three baseline classification techniques using deep feature, deep texture feature and combination of both. In our analysis, we found that the combination of both deep feature and deep texture feature is more effective for document figure classification task than the individual features. The dataset and the code are publicly available at https://researchweb.iiit. ac.in/~jobin.kv/projects/
Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks

In this study, a deep reinforcement learning (DRL) method was employed to solve the joint optimization problem for user association, resource allocation, and power allocation in heterogeneous networks (HetNets), which is an NP-hard problem. Existing studies have taken various optimization objectives into account. The heterogeneous network-deep-Q- network frame-work (HetDQN) is proposed to solve this type of optimization problem in HetNets. Based on maximum spectral efficiency, we designed a 6- layer deep neural network. The state space, objective function, and reward function are presented. In comparison with the existing solution, HetDQN can achieve a higher spectral efficiency. The simulation results revealed that HetDQN has better performance in term of convergence.
Learning to Drag: The Effects of Social Interactions in Touch Gestures Learnability for Older Adults

Considering the potential physical limitations of older adults, the naturalness of touch-based gestures as an interaction method is questionable. Assuming touch-based gestures are natural, they should be highly learnable and amenable to enhancement through social interactions. To investigate whether social interactions can enhance the learnability of touch gestures for older adults with low digital literacy, we conducted a study with 42 technology- naïve participants aged 64 to 82. They were paired and encouraged to play two games on an interactive tabletop with the expectation to use the drag gesture to complete the games socially. We then compared these results with a previous study of technology-naïve older adults playing the same games individually. The results of the comparisons show that dyadic interactions had some benefits for the participants in helping them to become comfortable with the drag gesture by negotiation and imitation. Further qualitative analysis suggested that playing pairs generally helped learners to comfortably explore the digital environment using the newly acquired skill.
Delay Optimized Computation Offloading and Resource Allocation for Mobile Edge Computing

The mobile edge computing (MEC)-enabled cellular network provides a promising paradigm for emerging services with intensive computation and low delay requirement. However, when multiple computation-intensive and delay-sensitive services are required concurrently, how to minimize the delay and monetary cost on mobile devices (MDs) under the limited computation resource and communication resource remains a challenging issue. In order to solve the issue, we formulate computation offloading, communication resource and computation resource allocation as an optimization problem in MEC-enabled cellular network. The problem is non-convex. Hence, we formulate it as a multi-objective computation offloading and resource allocation (MCORA) game and prove the existence of Nash equilibrium (NE). To obtain the NE, we design a MCORA algorithm taking the uplink, downlink spectrum resource, computation resource and offloading strategy into consideration. Simulation results show the MCORA algorithm can minimize system cost of all MDs. Moreover, in comparison to the existing algorithms, the proposed algorithm can achieve better performance in delay and system cost.
Component analysis of four-part extracts from Chamaecyparis obtusa Endl. by supercritical fluid extraction and anti-inflammatory effect on RAW 264.7cells

The main purpose of this study was to suggest a solution for inflammation caused by urban daily life based on supercritical extraction of Chamaecyparis obtusa Endl. (C. obtusa). The leaves, cone, wood, and bark of C. obtusa were separately extracted with supercritical extraction. Alpha-pinene, anti-inflammatory active compound of C. obtusa, was analyzed by electrospray ionization-mass spectrometry, gas chromatography, and gas chromatography mass spectrometry from each part of extracts. As a result of the reduction rate of nitric oxide in RAW 264.7 cells, the wood extract exhibited the highest reduction rate was about 45% among extracts from four part of C. obtusa. The wood extract from C. obtusa can be developed as a good anti-inflammatory natural raw material for natural product industry.
Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks

Continuous, dynamic and short-term learning is an effective learning strategy when operating in dynamic and adversarial environments, where concept drift constantly occurs and attacks rapidly change over time. In an on-line, stream learning model, data arrives as a stream of sequentially ordered samples, and older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. Stream approaches work in a limited amount of time, and have the advantage to perform predictions at any point in time during the stream. We focus on a particularly challenging problem, that of continually learning detection models capable to recognize cyber-attacks and system intrusions in a highly dynamic and adversarial environment such as the open Internet. We consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using (dynamic) sliding windows -- representing the system memory, to periodically re-learn, automatically adapting to concept drifts in the underlying data. By continuously learning and detecting concept drifts to adapt memory length, we show that adaptive learning algorithms can realize high detection accuracy of evolving network attacks over dynamic network data streams.
SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

A deep learning–based model predicts activity of a widely used genome editing tool in a highly accurate manner. We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity–predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.
Tunable-Deformed Graphene Layers for Actuation

Benefiting from unique planar structure, high flexibility, splendid thermal, and electric properties; graphene as a crucial component has been widely applied into smart materials and multi-stimulus responsive actuators. Moreover, graphene with easy processing and modification features can be decorated with various functional groups through covalent or non-covalent bonds, which is promising in the conversion of environmental energy from single and/or multi-stimuli, to mechanical energy. In this review, we present the actuating behaviors of graphene, regulated by chemical bonds or intermolecular forces under multi-stimuli and summarize the recent advances on account of the unique nanostructures in various actuation circumstances such as thermal, humidity, electrochemical, electro-/photo-thermal, and other stimuli.
City Scale Monitoring of On-Street Parking Violations with StreetHAWK

Unauthorized parking on city streets is a major contributor to traffic congestion and road accidents in developing nations. Due to the large scale and density of this problem, citywide (manual) monitoring of parking violations has not been effective with existing practices. To this end, we present StreetHAWK: an edge-centric, automated, real-time, privacy-preserving system; which leverages the rear camera of a dashboard mounted smartphone, and performs visual scene and location analytics to identify potential parking violations. We realize this system by overcoming the challenges of: (i) small object identification in various non-standard setups by extensive training on a deep learning based convolution detection model; (ii) limited violation assessment range of 15 m (a constraint of the phone's single camera unit) by augmenting it with a short-term historian and GPS for meeting the 100 m measurement violation guideline; and (iii) erroneous mobile scene analysis instances by lightweight filtering techniques that piggyback on the mobility of the camera and multi-modal sensing clues. The evaluation results obtained from real-world datasets show that StreetHAWK: (i) has three times higher accuracy in identifying small sized objects than other competing embedded detectors; and (ii) localizes these objects from a moving vehicle with a worst-case error of less than 5 m. On-the-road experiments show that StreetHAWK, running at a speed of 5 frames per second (FPS) on a typical Android smartphone, was able to detect (on an average) 80% of the parking violations.
Transcriptome and Volatilome Analysis During Growth of Brochothrix thermosphacta in Food: Role of Food Substrate and Strain Specificity for the Expression of Spoilage Functions

Brochothrix thermosphacta is one of the main spoilers in food, responsible for meat and seafood spoilage through the production of malodorous volatile organic compounds. The molecules produced by this bacterium depend on the substrate (meat or seafood) and the storage conditions such as gas mixtures used in the packaging. It seems also that the spoilage potential is strain dependent as production of diacetyl and acetoin, two molecules responsible for seafood spoilage, varies with strains. Therefore, this suggests the involvement of different metabolic functions depending on both food substrate and strain capacities. In this study, we selected two strains with different abilities to produce diacetyl and acetoin and compared their behavior after grown in beef or cooked peeled shrimp juices. We determined the genes upregulated by both strains depending on the growth substrate and those that were specifically upregulated in only one strain. The genes upregulated by both strains in meat or in shrimp juice revealed the importance of the substrate for inducing specific metabolic pathways. The examination of genes that were specifically upregulated in only one of the two strains revealed strain features associated to specific substrates and also strain-specific regulations of metabolic pathways putatively leading to different levels of spoilage molecule production. This shows that the spoilage potential of B. thermosphacta depends on nutrients provided by food substrate and on metabolic activity potential that each strain possesses.
Detecting Community Structures Based on an Improved Discrete Bat Algorithm

Research on discovering the community structure has become a popular issue in the field of network analysis.In this paper, an improved discrete bat algorithm is proposed to solve the community detection problem. First, an ordered adjacent list method is used to encode the position of bat for population initialization. In the proposed method, Modularity is applied as the objective function. All the bats are divided into some groups based on their fitness, and the bat position in each group is updated based on operators we defined. It will expand the search area and improve the diversity of population. Local optimal solution and global optimal solution can be generated through strategy of dividing and merging bat position. Simulations and comparison results based on synthetic and real networks are performed to prove the effectiveness and accuracy of the proposed method in detecting community structures in networks.
MERCAT: A Metric for the Evaluation and Reconsideration of Certificate Authority Trustworthiness

Public key infrastructures (PKIs) build the foundation for secure communication of a vast majority of cloud services. In the recent past, there has been a series of security incidents leading to increasing concern regarding the trust model currently employed by PKIs. One of the key criticisms is the architecture's implicit assumption that certificate authorities (CAs) are trustworthy a priori. This work proposes a holistic metric to compensate this assumption by a differentiating assessment of a CA's individual trustworthiness based on objective criteria. The metric utilizes a wide range of technical and non-technical factors derived from existing policies, technical guidelines, and research. It consists of self-contained submetrics allowing the simple extension of the existing set of criteria. The focus is thereby on aspects which can be assessed by employing practically applicable methods of independent data collection. The metric is meant to help organizations, individuals, and service providers deciding which CAs to trust or distrust. For this, the modularized submetrics are clustered into coherent submetric groups covering a CA's different properties and responsibilities. By applying individually chosen weightings to these submetric groups, the metric's outcomes can be adapted to tailored protection requirements according to an exemplifying attacker model.
HP-Mapper: A High Performance Storage Driver for Docker Containers

Docker containers are widely deployed to provide lightweight virtualization, and they have many desirable features such as ease of deployment and near bare-metal performance. However, both the performance and cache efficiency of containers are still limited by their storage drivers due to the coarse-grained copy-on-write operations, and the large amount of redundancy in both I/O requests and page cache. To improve I/O performance and cache efficiency of containers, we develop HP-Mapper, a high performance storage driver for Docker containers. HP-Mapper provides a two-level mapping strategy to support fine-grained copy-on-write with low overhead, and an efficient interception method to reduce redundant I/Os. Furthermore, it uses a novel cache management mechanism to reduce duplicate cached data. Experiment results with our prototype system show that HP-Mapper significantly reduces copy-on-write latency due to its finer-grained copy-on-write scheme. Moreover, HP-Mapper can also reduce 65.4% cache usage on average due to elimination of duplicated data. As a result, HP-Mapper improves the throughput of real-world workloads by up to 39.4%, and improves the startup speed of containers by 2.0x.
An Efficient Message Passing Algorithm for Active User Detection and Channel Estimation in NOMA

In 5G wireless communication network, massive machine type communication (mMTC) is an emerging research topic. For mMTC, non-orthogonal multiple access (NOMA) has been proposed to support its large-scale connectivity. Due to the sparsity of mMTC, compressed sensing based algorithms can be used to identify the active users and recover the sparse channel state information (CSI) vector. In this paper, we propose a Bayesian message passing algorithm based on expectation propagation (EP) for joint active user detection (AUD) and channel estimation (CE) in NOMA. The proposed method approximates the complicated target distribution with a Gaussian distribution to achieve linear complexity. Simulations demonstrate that the EP-based algorithm achieves better performance in joint AUD and CE than the exiting algorithms, especially in the low SNR regime.
Home Worlds: Situating Domestic Computing in Everyday Life Through a Study of DIY Home Repair

We draw on a qualitative study of homeowners who engage in do-it-yourself (DIY) home repair and introduce "home worlds" as a conceptual lens to understand how interactions with the home as a built environment (local and mechanical engagements with its material components) are situated in broad, diffuse, and constraint-rich social worlds. A "home worlds" perspective challenges domestic computing inquiries to adopt a broader orientation when considering a home's users and requirements; our case of home repair, in particular, provokes us to consider the home as embedded in communities and framed in relation to the precarious constraints of everyday life. We discuss how these insights open up new avenues for future research and design of collaborative domestic computing technologies.
Implementation of C-RAN Architecture with CU-CP and CU-UP Separation Based on SDR/NFV

Cloud-Radio Access Network (C-RAN) architecture has been considered widely in wireless network research. Software Defined Radio (SDR) and Network Function Virtualization (NFV) technologies realize flexible deployments of C-RAN. In our paper, a testbed based on SDR and NFV is built for splitting C-RAN into several independent units: Centralized Unit Control Plane (CU-CP), Centralized Unit User Plane (CU-UP) and Distributed Unit (DU). To realize communications among the different units, we focus on the scheme of communication framework and the modification of signaling flow. In addition, the three units are deployed on different virtual machines as an experimental testbed for functional validation and performance testing. Finally, we provide socket and Data Plane Development Kit (DPDK) with three different encryption algorithms in CU-CP/CU-UP testing. The results show the proposed architecture indeed has an effective performance.