Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Assignment in Mobile Crowdsensing: Present and Future Directions

Mobile crowdsensing has wide application perspectives and tremendous advantages over traditional sensor networks due to its low cost, extensive coverage, and high sensing accuracy properties. Task assignment is a crucial issue in mobile crowdsensing systems which is intended to achieve a good tradeoff between task quality and task cost. The design of efficient task assignment mechanisms has attracted a lot of attention and much work has been carried out. In this article, we present a comprehensive survey of state-of-the-art task assignment mechanisms in mobile crowdsensing systems. We will first introduce several fundamental issues in task assignment and classify existing mechanisms based on different design criteria. Then we introduce how each of the existing mechanisms works and discuss their merits and deficiencies. Finally, we discuss challenging issues and point out some future directions in this area.
Analysis of Electrochemical Characteristics of Fabricated Polyaniline Nanomaterial for Super capacitors and storage Devices

In this work, fabrication of Polyaniline (PANI) nanomaterial is performed by employing chemical oxidative technique. Fabrication of the nanomaterial is preceded by analysis of its electrochemical characteristics for distinct applications in numerous power devices. Especial focus is on energy and power saving device i.e. super capacitor. Electrochemical investigation is accomplished with the assistance of CV, EIS, GCD and I-V tests. The electrochemical analysis has assisted to prove it be one of the most promising nanomaterials for power storage devices.
Computer Graphics Identification Combining Convolutional and Recurrent Neural Networks

In this letter, a deep-learning-based pipeline is proposed to distinguish photographics (PGs) from computer-graphics (CGs) combining convolutional neural network (CNN) and recurrent neural network (RNN). In the preprocessing stage, the color space transformation and the Schmid filter bank are utilized to extract chrominance and luminance components, which suppress the irrelevant information of various image contents for the CG identification task. Then, a dual-path CNN architecture is designed to learn joint feature representations of local patches for exploiting their color and texture characteristics. To extract the global artifact, the directed acyclic graph RNN is applied to model the spatial dependence of local patterns. Finally, the output score of RNN is used to identify the input sample. The CG/PG dataset is constructed by collecting samples from the Internet. Experimental results show that the proposed framework can outperform state-of-the-art methods on identification ability of CGs, especially for images with low resolution.
On Power Law Growth of Social Networks

What is the growth dynamics of social networks, like Facebook or WeChat? Does it truly exhibit exponential early-growth, as predicted by the celebrated models, like the Bass model? How about the dynamics of links, for which there are few published models? For the first time, we examine the growth of WeChat which is the largest online social network in China, together with several other real social networks. We observe Power-Law growth dynamics for both nodes and links, a fact that breaks the textbook models featuring Sigmoid curves. We propose <sc>NetTide</sc>, along with differential equations for the growth of nodes and links. Our model fits the growth dynamics of real social networks well; it encompasses many traditional growth dynamics as special cases, while remaining parsimonious in parameters. The <sc>NetTide</sc> for link growth is the first one of its kind, accurately fitting real data, and capturing densification phenomenon. We further formulate two stochastic generators, which interpret the growth of nodes and links through survival analysis and micro-level interactions within a social network, respectively. The proposed generators reproduce realistic growth dynamics of social networks. When applied on the WeChat data, our <sc> NetTide</sc> forecasted <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math><alternatives> <inline-graphic xlink:href="zang-ieq1-2801844.gif"/></alternatives></inline-formula> 730 days ahead with 3 percent error.
A Method for Fast and Reliable Analysis and Optimization of Side-Coupled Cavity Filters and Multiplexers

This paper presents an efficient segmentation method of a filter made up of side-coupled cavities that use a reduced complexity model. The model is built by using the reactance matrix of only three parallel waveguides, either coaxial or hollow, coupled via apertures among them. This allows building the databases necessary for fast and reliable analysis and optimization of very complex filters and multiplexers. The method was used to design a low-cost eight-channel coaxial-cavity input multiplexer.
Spartan: A Framework For Sparse Robust Addressable Networks

A Peer-to-Peer (P2P) network is a dynamic collection of nodes that connect with each other via virtual overlay links built upon an underlying network (usually, the Internet). Typical P2P networks are highly dynamic and can experience very heavy churn, i.e., a large number of nodes join/leave the network every time step. We present an overlay framework called Sparse Robust Addressable Network (Spartan) that can tolerate heavy adversarial churn. We show that Spartan can be built efficiently in a fully distributed manner within O(log n) rounds. Furthermore, the Spartan overlay structure can be maintained, again, in a fully distributed manner despite adversarially controlled churn (i.e., nodes joining and leaving) and significant variation in the number of nodes. When the number of nodes in the network lies in [n, fn] for any fixed f > 1 the adversary can remove up to ? n nodes and add up to ? n nodes (for some small but fixed ? > 0) within any period of P rounds for some P ? O(log log n). Moreover, the adversary can add or remove nodes from the network at will and without any forewarning. Despite such uncertainty in the network, Spartan maintains ? (n/log n) committees that are stable and addressable collections of ? (log n) nodes each. Any node that enters the network will be able to gain membership in one of these committees within O(1) rounds. The committees are also capable of performing sustained computation and passing messages between each other. Thus, any protocol designed for static networks can be simulated on Spartan with minimal overhead. This makes Spartan an ideal platform for developing applications. All our results hold with high probability.
Exploiting geo-social correlations to improve pairwise ranking for point-of-interest recommendation

Recently, as location-based social network (LBSN) rapidly grow, point-of-in-terest (POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-in-terests (POIs) on the actual output of recom-mender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model (GSB-PR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.
Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study

Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood–brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine‐learning methodologies to identify a T2DM‐related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity.
What Size Should Your Buffers to Disks be?

Burst-Buffers are high throughput, small size intermediate storage systems typically based on SSDs or NVRAM that are designed to be used as a potential buffer between the computing nodes of a supercomputer and its main storage system consisting of hard drives. Their purpose is to absorb the bursts of I/O that many HPC applications experience (for example for saving checkpoints or data from intermediate results). In this paper, we propose a probabilistic model for evaluating the performance of Burst-Buffers. From a model of application and a data management strategy, we build a Markov-chain-based model of the system, that allows us to quickly answer issues about dimensioning of the system: for a given set of applications, and for a given Burst-Buffer size and bandwidth, how often does the buffer overflow? We also provide extensive simulation results to validate our modeling approach.
Classification and Anomaly Detection in Traffic Patterns of New York City Taxis: A Case Study in Compound Analytics

Both graph-theoretical algorithms and machine learning techniques have been applied individually to solve a wide variety of transportation, network and routing problems. Many applications, however, are amenable to a combined approach. In this paper, we present a case study on the analysis of New York City taxi traffic using our compound analytics framework. Using incremental probabilistic learning, we infer a graph from each day's traffic. We then calculate a set of graph-theoretic metrics designed to capture a broad overview of the topology of the graphs. Using machine learning to reason over these metrics, we classify the graphs as coming from weekend or weekday traffic, and identify graphs affected by a range of disruptive events.
Policy-Aware Service Composition: Predicting Parallel Execution Performance of Composite Services

With the increasing volume of data to be analysed, one of the challenges in Service Oriented Architecture (SOA) is to make web services efficient in processing large-scale data. Parallel execution and cloud technologies are the keys to speed-up the service invocation. In SOA, service providers typically employ policies to limit parallel execution of the services based on arbitrary decisions. In order to attain optimal performance improvement, users need to adapt to the services policies. A composite service is a combination of several atomic services provided by various providers. To use parallel execution for greater composite service efficiency, the degree of parallelism (DOP) of the composite services need to be optimized by considering the policies of all atomic services. We propose a model that embeds service policies into formulae to calculate composite service performance. From the calculation, we predict the optimal DOP for the composite service, where it attains the best performance. Extensive experiments are conducted on real-world translation services. We use several measures such as mean prediction error (MPE), mean absolute deviation (MAD) and tracking signal (TS) to evaluate our model. The analysis results show that our proposed model has good prediction accuracy in identifying optimal DOPs for composite services.
Sensitive detection of Live Escherichia coli by bacteriophage amplification-coupled immunoassay on the Luminex® MAGPIX instrument

Phages are natural predators of bacteria and have been exploited in bacterial detection because of their exquisite specificity to their cognate bacterial hosts. In this study, we present a bacteriophage amplification-coupled assay as a surrogate for detecting a bacterium present in a sample. The assay entails detection of progeny phage resulting from infection and subsequent growth inside the bacterium present in suspected samples. This approach reduces testing time and enhances sensitivity to identify pathogens compared to traditional overnight plaque assay. Further, the assay has the ability to discriminate between live and dead cells since phages require live host cells to infect and replicate. To demonstrate its utility, phage MS2 amplification-coupled, bead-based sandwich type immunoassay on the Luminex® MAGPIX instrument for Escherichia coli detection was performed. The assay not only showed live cell discrimination ability but also a limit of E. coli detection of 1×102 cells/mL of live cells after a 3-hour incubation. In addition, the sensitivity of the assay was not impaired in the presence of dead cells. These results demonstrate that bacteriophage amplification-coupled assay can be a rapid live cell detection assay compared to traditional culture methods and a promising tool for quick validation of bacterial inactivation. Combined with the unique multiplex bead chemistry afforded by Luminex® MAGPIX platform, the phage assay can be expanded to be an ultra-deep multiplex assay for the simultaneous detection of multiple pathogens using specific phages directed against the target pathogens.
Forensic-Aware Anti-DDoS Device

When defending DDoS and other types of network attack, most products or service providers perform the protection by dropping the attack traffics. It cures the symptoms but not the disease. To help eliminate network attack, a more proactive approach is to trace back the attack source and stop the attack before it starts. Collecting the attack data is essential in attack trace-back. In this paper, we propose a live capture device to record the attack efficiently without disturbing the original network performance. The device is also integrated with anti-DDoS technique so that forensic data collection when be performed even under DDoS attacks. We made use of a network bridge and utilized packet capturing functionality provided by Linux, plus our packet storing mechanisms to build the forensic aware data collection device. The anti-DDoS protection uses machine learning to extract features of attacks, and then use a customized Bloom filter to defend attacks based on selected features. We implemented and tested the performance of the proposed technique in a lab environment.
Runtime Scheduling Policies for Distributed Graph Algorithms

In this paper we explore scheduling and runtime system support for unordered distributed graph computations that rely on optimistic (speculative) execution. Performance of such algorithms is impacted by two competing trends: the higher degree of parallelism enabled by optimistic execution in turn requires substantial runtime support. To address the potentially high overhead and scheduling complexity introduced by the runtime, we investigate customizable scheduling policies that augment the scheduler of the underlying runtime to adapt it to a specific graph application. We present several implementations of Distributed Control (DC), a data-driven unordered approach with work prioritization and demonstrate that customizable scheduling policies result in the most efficient implementation, outperforming the well-known ? -stepping Single-Source Shortest Paths (SSSP) and Jones-Plassmann vertex-coloring algorithms. We apply two scheduling techniques, flow control and adaptive frequency of network progress, which allow application-level control over the balance of domain work and the runtime work. Experimental results show the benefit of such application-aware scheduling for irregular distributed graph algorithms.
Systemic Inflammation Mediates Age-Related Cognitive Deficits

The association between systemic inflammation and cognitive deficits is well-documented. Further, previous studies have shown that systemic inflammation levels increase with age. The present study took a novel approach by examining the extent to which systemic inflammation levels mediated age-related cognitive decline. Forty-seven young and 46 older generally healthy adults completed two cognitive tasks measuring processing speed and short-term memory, respectively. Serum concentrations of three inflammatory biomarkers (including interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP)) were measured in each participant. Both cognitive measures showed age-related deficits. In addition, levels of IL-6 and TNF-α were elevated with age. IL-6 partially mediated the difference in processing speed between the young and the older participant age group; there was no mediation effect for TNF-α and CRP. Considering chronological age, IL-6 partially accounted for age-related impairment in processing speed within older but not young participants. No effects were found for short-term memory. Evidence from this research supports the role of inflammatory processes in age-related cognitive decline. Processes involved in this mediation and differences in inflammatory influence on specific cognitive functions are discussed.
Automatically Repairing SQL Faults

SQL is the standard database language, yet SQL statements can be complex and expensive to debug by hand. Automatic program repair techniques have the potential to reduce cost significantly. A previous attempt to repair SQL faults automatically used a decision tree (DT) algorithm that succeeded in some cases, but also generated many patches that passed the automated tests but that were not acceptable to the engineers. This paper proposes a novel fault localization and repair technique to repair faulty SQL statements. It targets faults in two common SQL constructs, JOIN and WHERE. It identifies the fault location and type precisely, and then creates a patch to fix the fault. We implemented this technique in a tool, and evaluated it on five medium to large-scale databases using 825 faulty queries with various complexity and faulty types. Experimental results showed that this technique can identify and repair JOIN faults when the DT approach is infeasible, and repair WHERE faults at about the same rate as the DT approach. Moreover, patches generated by our approach are more acceptable to engineers, and the tool is much faster.
Serum leptin and homeostasis model assessment-IR as novel predictors of early liver fibrosis in chronic hepatitis B virus infection

ABSTRACT Background: The relationship between hepatitis B virus (HBV) infection, leptin and insulin resistance remains unclear. We hypothesised links between serum leptin and insulin resistance in non-diabetic patients with chronic viral hepatitis B infection and their relation to liver fibrosis. Methods: We recruited 190 untreated patients with chronic HBV infection and 72 healthy controls. Serum leptin, fasting glucose, insulin, liver function tests (LFTs), C-peptide and Homeostasis model assessment-IR (HOMA-IR) were measured/calculated by ELISA and standard techniques. Results: Serum leptin, C-peptide (both P < 0.001), HOMA-IR (P = 0.021) and several LFTs were increased in patients with chronic HBV-infection. In multivariate regression analysis, both HOMA-IR (P = 0.003) and leptin (P = 0.002) were significant independent predictors of HBV infection. There were significant positive correlations (P < 0.01) between leptin and HOMA-IR (r = 0.81), between serum leptin and METAVIR activity (r = 0.95), and between HOMA-IR and BMI (r = 0.75), fasting glucose (r = 0.005), and fasting insulin (r = 0.81). Several LFTs, glucose and insulin correlated modestly (r = 0.61–0.69, P < 0.05) with leptin. Conclusion: Serum leptin may be related to the rate of fibrosis progression in nondiabetic patients with chronic HBV infection. Follow-up by serial measurement of serum leptin and HOMA-IR in non diabetic HBV-infected patients may be used as a non-invasive marker of early liver fibrosis.
Real-Time Fully Incremental Scene Understanding on Mobile Platforms

We propose an online RGB-D based scene understanding method for indoor scenes running in real time on mobile devices. First, we incrementally reconstruct the scene via simultaneous localization and mapping and compute a three-dimensional (3-D) geometric segmentation by fusing segments obtained from each input depth image in a global 3-D model. We combine this geometric segmentation with semantic annotations to obtain a semantic segmentation in form of a semantic map. To accomplish efficient semantic segmentation, we encode the segments in the global model with a fast incremental 3-D descriptor and use a random forest to determine its semantic label. The predictions from successive frames are then fused to obtain a confident semantic class across time. As a result, the overall method achieves an accuracy that gets close to the most state-of-the-art 3-D scene understanding methods while being much more efficient, enabling real-time execution on low-power embedded systems.
Energy-Aware Concurrent Multipath Transfer for Real-Time Video Streaming Over Heterogeneous Wireless Networks

The advancements in networking technologies and hand-held devices enable mobile users to concurrently receive real-time multimedia streaming (e.g., high-definition video) with different radio interfaces (e.g., Wi-Fi and LTE networks). Stream control transmission protocol (SCTP) is an important transport-layer solution to implement concurrent multipath transfer (CMT) over heterogeneous wireless networks with multihomed terminals. However, it is challenging to distribute multimedia content to resource-limited mobile devices because of the contradiction between energy consumption and streaming quality. To deliver the energy-efficient and quality-aware multimedia streaming over multiple wireless networks, this paper presents an Energy and goodPut Optimized CMT (EPOC) solution. First, we develop an analytical framework to model the relationship between energy consumption and goodput performance for real-time multimedia transmission to multihomed mobile devices. Second, we propose a joint forward error correction coding and rate allocation scheme to minimize energy consumption while satisfying goodput constraint. EPOC effectively leverages the energy-goodput tradeoff and multipath diversity to optimize mobile multimedia transmission in heterogeneous networking environment. We conduct the performance evaluation through extensive semi-physical emulations in Exata involving H.264 video streaming. Compared with the reference CMT schemes using SCTP, EPOC achieves appreciable improvements in energy conservation, goodput, and video peak signal-to-noise ratio.