Query: Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

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Robust Real-time Object Detection <=SEP=> This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

The mnist database of handwritten digits <=SEP=> please note that your browser may uncompress these files without telling you. If the files you downloaded have a larger size than the above, they have been uncompressed by your browser. Simply rename them to remove the .gz extension. Some people have asked me "my application can't open your image files". These files are not in any standard image format. You have to write your own (very simple) program to read them. The file format is described at the bottom of this page.

Boosting the margin: A new explanation for the effectiveness of voting methods <=SEP=> One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik’s support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.

Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks <=SEP=> Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justi es some level of quantization in order to achieve faster feature extraction. Our approach consists of approximating regions of the signal with low degree polynomials, and then di erentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite e ectively. The true convolution can be recovered by integrating the result of the convolution. This method yields substantial speed up in feature extraction and is applicable to convolutional neural networks.

Statistical Pattern Recognition <=SEP=> This course will provide an introduction to statistical pattern recognition. The lectures will focus on different techniques including methods for feature extraction, dimensionality reduction, data clustering and pattern classification. State-of-art approaches such as ensemble learning and sparse modelling will be introduced. Selected real-world applications will illustrate how the techniques are applied in practice.

Issues and challenges of data security in a cloud computing environment <=SEP=> Now customers can opt for software and information technology services according to his requirements and can get these services on a leased basis from the network service provider and this has the facility to scale its requirements to up or down. This service is known as cloud computing, provided by the infrastructure provider which is a third party provider. Cloud computing provides many advantages to the customer like scalability, better economics of scaling, its ability to recover from problems, its ability to outsource non-core activities and flexibility. Cloud computing is a better option for the organizations to take as their best option without any initial investment and day by day frequent and heavy use of cloud computing is increasing but despite all the benefits a cloud offers to an organization there are certain doubts and threats regarding the security issues associated with a cloud computing platform. The security issues primarily involve the external control over organizational structure and management and personal and private data of the organization can be compromised. Personal and private data in this computing environment has a very high risk of breach of confidentiality, integrity and availability. Growth of cloud computing is mainly hampered due to these security concerns and challenges. Proper security arrangements are need to be placed before selecting the service provider for any cloud computing service and customers need to be very careful about understanding the risks of security breaches and challenges of using this new computing environment. This detailed study discusses about some of the challenges associated with cloud computing services and security issues related to this platform.

Radar target detection and Doppler ambiguity resolution <=SEP=> A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature [2]. Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.

A direct LDA algorithm for high-dimensional data - with application to face recognition <=SEP=> However, for a task with very high-dimensional data such as images, the traditional LDA algorithm encounters several di$culties. Consider face recognition for example. A low-de"nition face image of size 64 64 implies a feature space of 64 64"4096 dimensions, and therefore scatter matrices of size 4096 4096"16M. First, it is computationally challenging to handle big matrices (such as computing eigenvalues). Second, those matrices are almost always singular, as the number of training images needs to be at least 16M for them to be non-degenerate. Due to these di$culties, it is commonly believed that a direct LDA solution for such high-dimensional data is infeasible. Thus, ironically, before LDA can be used to reduce dimensionality, another procedure has to be "rst applied for dimensionality reduction. In face recognition, many techniques have been proposed (for a good review, see Ref. [1]). Among them, the most notable is a two-stage PCA#LDA approach [2,3]:

A Wideband SiGe BiCMOS Frequency Doubler With 6.5-dBm Peak Output Power for Millimeter-Wave Signal Sources <=SEP=> This paper presents a balanced frequency doubler with 6.5-dBm peak output power at 204 GHz in 130-nm SiGe BiCMOS technology ( $f_{T}/f_{\max }=210$ /250 GHz). To convert the single-ended input signal to a differential signal for balanced operation, an on-chip transformer-based balun is employed. Detailed design procedure and compensation techniques to lower the imbalance at the output ports, based on mixed mode S parameters are proposed and verified analytically and through electromagnetic simulations. The use of optimized harmonic reflectors at the input port results in a 2-dBm increase in output power without sacrificing the bandwidth of interest. The measured conversion loss of the frequency doubler is 9 dB with 6-dBm input power at 204-GHz output. The measured peak output power is 6.5 dBm with an on-chip power amplifier stage. The 3-dB output power bandwidth is measured to be wider than 50 GHz (170–220 GHz). The total chip area of the doubler is 0.09 mm2 and the dc power consumption is 90 mW from a 1.8-V supply, which corresponds to a 5% collector efficiency.

Crowdfunding: A Spimatic application of digital fandom <=SEP=> The digital environment has opened up new opportunities for fans to engage with the production process of their favoured texts. The successful Kickstarter campaigns for Rob Thomas and Kristen Bell’s Veronica Mars film and Zach Braff’s film Wish I Was Here, and the failed Kickstarter campaign for Melissa Joan Hart’s film Darci’s Walk of Shame, each illustrate how the entertainers drew (or did not draw) on specific support from his or her own fan communities to generate funds and word-of-mouth publicity. In this paper I augment studies of digital fandom by integrating the technosocial concept of the Spime into the relationships forged between the technology of crowdfunding and the affect of particular audiences. I illustrate how ‘crowdfunding fandom’ changes the unique posthuman experience of 21 century participatory culture by interpreting fandom in a Spimatic network. This ultimately more fully integrates the fan into the production process, creating a truly ‘participatory culture’.

A computational cognition model of perception, memory, and judgment <=SEP=> The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.

A Flexible Architecture for Ray Tracing Terrain Heightfields <=SEP=> High-quality interactive rendering of terrain surfaces is a challenging task, which requires compromises between rendering quality, rendering time and available resources. However, current solutions typically provide optimized strategies tailored to particular constraints. In this paper we propose a more scalable approach based on functional programming and introduce a flexible ray tracer for rendering terrain heightfields. This permits the dynamic composition of complex and recursive shaders. In order to exploit the concurrency of the GPU for a large number of dynamically created tasks with inter-dependencies, the functional model is represented as a token stream and is iteratively rewritten via pattern matching on multiple shader cores in parallel. A first prototype demonstrates the feasibility of our approach.

Presurgical evaluation of epilepsy. <=SEP=> An overview of the following six cortical zones that have been defined in the presurgical evaluation of candidates for epilepsy surgery is given: the symptomatogenic zone; the irritative zone; the seizure onset zone; the epileptogenic lesion; the epileptogenic zone; and the eloquent cortex. The stepwise historical evolution of these different zones is described. The current diagnostic techniques used in the definition of these cortical zones, such as video-EEG monitoring, MRI and ictal single photon emission computed tomography, are discussed. Established diagnostic tests are set apart from procedures that should still be regarded as experimental, such as magnetoencephalography, dipole source localization and spike-triggered functional MRI. Possible future developments that might lead to a more direct definition of the epileptogenic zone are presented.

Deep Discriminative Representation Learning with Attention Map for Scene Classification <=SEP=> Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.

Adaptive block truncation filter for MVC depth image enhancement <=SEP=> In Multiview Video plus Depth (MVD) format, virtual views are generated from decoded texture videos with decoded depth images through Depth Image based Rendering (DIBR). 3DV-ATM is a reference model for H.264/AVC based Multiview Video Coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding of 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized views. This paper presents a low complexity adaptive block truncation filter to recover the sharp object boundaries of depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results show that sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.

Detecting Encrypted Traffic: A Machine Learning Approach <=SEP=> Detecting encrypted traffic is increasingly important for deep packet inspection (DPI) to improve the performance of intrusion detection systems. We propose a machine learning approach with several randomness tests to achieve high accuracy detection of encrypted traffic while requiring low overhead incurred by the detection procedure. To demonstrate how effective the proposed approach is, the performance of four classification methods (Näıve Bayesian, Support Vector Machine, CART and AdaBoost) are explored. Our recommendation is to use CART which is not only capable of achieving an accuracy of 99.9% but also up to about 2.9 times more efficient than the second best candidate (Näıve Bayesian).

Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data <=SEP=> Topic modeling has been commonly used to discover topics from document collections. However, unsupervised models can generate many incoherent topics. To address this problem, several knowledge-based topic models have been proposed to incorporate prior domain knowledge from the user. This work advances this research much further and shows that without any user input, we can mine the prior knowledge automatically and dynamically from topics already found from a large number of domains. This paper first proposes a novel method to mine such prior knowledge dynamically in the modeling process, and then a new topic model to use the knowledge to guide the model inference. What is also interesting is that this approach offers a novel lifelong learning algorithm for topic discovery, which exploits the big (past) data and knowledge gained from such data for subsequent modeling. Our experimental results using product reviews from 50 domains demonstrate the effectiveness of the proposed approach.

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design <=SEP=> Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flowbased models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at https://github.com/aravind0706/flowpp.

Engineering exosomes as refined biological nanoplatforms for drug delivery <=SEP=> Exosomes, a subgroup of extracellular vesicles (EVs), have been recognized as important mediators of long distance intercellular communication and are involved in a diverse range of biological processes. Because of their ideal native structure and characteristics, exosomes are promising nanocarriers for clinical use. Exosomes are engineered at the cellular level under natural conditions, but successful exosome modification requires further exploration. The focus of this paper is to summarize passive and active loading approaches, as well as specific exosome modifications and examples of the delivery of therapeutic and imaging molecules. Examples of exosomes derived from a variety of biological origins are also provided. The biocompatible characteristics of exosomes, with suitable modifications, can increase the stability and efficacy of imaging probes and therapeutics while enhancing cellular uptake. Challenges in clinical translation of exosome-based platforms from different cell sources and the advantages of each are also reviewed and discussed.

A Compositional Interpretation of Biomedical Event Factuality <=SEP=> We propose a compositional method to assess the factuality of biomedical events extracted from the literature. The composition procedure relies on the notion of semantic embedding and a fine-grained classification of extrapropositional phenomena, including modality and valence shifting, and a dictionary based on this classification. The event factuality is computed as a product of the extra-propositional operators that have scope over the event. We evaluate our approach on the GENIA event corpus enriched with certainty level and polarity annotations. The results indicate that our approach is effective in identifying the certainty level component of factuality and is less successful in recognizing the other element, negative polarity.

CIM: Community-Based Influence Maximization in Social Networks <=SEP=> Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.

A Dictionary Approach to EBSD Indexing <=SEP=> We propose a framework for indexing of grain and sub-grain structures in electron backscatter diffraction (EBSD) images of polycrystalline materials. The framework is based on a previously introduced physics-based forward model by Callahan and De Graef (2013) relating measured patterns to grain orientations (Euler angle). The forward model is tuned to the microscope and the sample symmetry group. We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary. These patterns are used to identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixels 3× 3 neighborhood. Indexing is accomplished by computing the mean orientation of the closest dictionary matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von MisesFisher distributions over the quaternionic 3-sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification. We demonstrate the proposed dictionary-based approach on a Ni-base IN100 alloy. c1 c1 Part of this work was reported in the Proceedings of the IEEE International Conference on Image Processing (ICIP), Melbourne Australia, Sept 2013.

The Impact of Power Allocation on Cooperative Non-orthogonal Multiple Access Networks With SWIPT <=SEP=> In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models <=SEP=> The Programming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system.

Boosting automatic event extraction from the literature using domain adaptation and coreference resolution <=SEP=> MOTIVATION
In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage.


RESULTS
We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task.


AVAILABILITY
The improved version of EventMine, incorporating the CR system and DA methods, is available at: http://www.nactem.ac.uk/EventMine/.

GP-GAN: Towards Realistic High-Resolution Image Blending <=SEP=> Recent advances in generative adversarial networks (GANs) have shown promising potentials in conditional image generation. However, how to generate high-resolution images remains an open problem. In this paper, we aim at generating high-resolution well-blended images given composited copy-and-paste ones, i.e. realistic highresolution image blending. To achieve this goal, we propose Gaussian-Poisson GAN (GP-GAN), a framework that combines the strengths of classical gradient-based approaches and GANs, which is the first work that explores the capability of GANs in high-resolution image blending task to the best of our knowledge. Particularly, we propose GaussianPoisson Equation to formulate the high-resolution image blending problem, which is a joint optimisation constrained by the gradient and colour information. Gradient filters can obtain gradient information. For generating the colour information, we propose Blending GAN to learn the mapping between the composited image and the well-blended one. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artefacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that majority of workers are in favour of the proposed approach.

Interpretability and Informativeness of Clustering Methods for Exploratory Analysis of Clinical Data <=SEP=> Clustering methods are among the most commonly used tools for exploratory data analysis. However, using clustering to perform data analysis can be challenging for modern datasets that contain a large number of dimensions, are complex in nature, and lack a ground-truth labeling. Traditional tools, like summarization and plotting of clusters, are of limited benefit in a high-dimensional setting. On the other hand, while many clustering methods have been studied theoretically, such analysis often has limited instructive value for practical purposes due to unverifiable assumptions, oracle-dependent tuning parameters, or unquantified finite-sample effects.

Learning user tastes: a first step to generating healthy meal plans <=SEP=> Poor nutrition is fast becoming one of the major causes of ill-health and death in the western world. It is caused by a variety of factors including lack of nutritional understanding leading to poor choices being made when selecting which dishes to cook and eat. We wish to build systems which can recommend nutritious meal plans to users, however a crucial pre-requisite is to be able to recommend dishes that people will like. In this work we investigate key factors contributing to how recipes are rated by analysing the results of a longterm study (n=123 users) in order to understand how best to approach the recommendation problem. In doing so we identify a number of important contextual factors which can influence the choice of rating and suggest how these might be exploited to build more accurate recipe recommender systems. We see this as a crucial first step in a healthy meal recommender. We conclude by summarising our thoughts on how we will combine recommended recipes into meal plans based on nutritional guidelines.

Anomalous Magnetic Field Activity During a Bioenergy Healing Experiment <=SEP=> A few studies have reported magnetic fi eld changes during bioenergy healing. In a pilot experiment, we examined magnetic fi eld activity during hands-on healing and distant healing of mice with experimentally induced tumors. During healing sessions, we observed distinct magnetic fi eld oscillations adjacent to the mice cages, which were similar in appearance to those reported by Zimmerman (1985). The magnetic fi eld oscillations began as 20–30 Hz oscillations, slowing to 8–9 Hz, and then to less than 1 Hz, at which point the oscillations reversed and increased in frequency, with an overall symmetrical appearance resembling a “chirp wave.” The waves ranged from 1–8 milliGauss peak-to-peak in strength and 60–120 sec in duration. Evidence to date suggests that bioenergy healing may be detectable with DC gaussmeters, although we have not ruled out an artifactual basis for the oscillations reported here.

Dynamic Resource Allocation in Ad-Hoc Mobile Cloud Computing <=SEP=> Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.