Abstract: Intelligent agent systems are often used to implement complex software systems. A key aspect of agent systems is goals: a programmer or user defines a set of goals for an agent, and then the agent is left to determine how best to satisfy the goals assigned to it. Such goals include both achievement goals and maintenance goals. An achievement goal describes a particular state the agent would like to become true, such as being in a particular location or having a particular bank balance. A maintenance goal specifies a condition which is to be kept satisfied, such as ensuring that a vehicle stays below a certain speed, or that it has sufficient fuel. Current agent systems usually only utilize reactive maintenance goals, in that the agent only takes action after the maintenance condition has been violated. In this paper, we discuss methods by which maintenance goals can be made proactive, i.e., acting before a maintenance condition is violated, having predicted that the maintenance condition will be violated in the future. We provide a representation of proactive maintenance goals, reasoning algorithms, an operational semantics that realizes these algorithms and an experimental evaluation of our approach.

Abstract: In many large-scale distributed systems and on the Web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable interaction environment. A traditional approach to reason about the risk of a transaction is to determine if the involved agent is trustworthy on the basis of its behavior history. As a departure from such traditional trust models, we propose a generic, trust framework based on machine learning where an agent uses its own previous transactions with other agents to build a personal knowledge base. This is used to assess the trustworthiness of a transaction on the basis of the associated features, particularly using the features that help discern successful transactions from unsuccessful ones. These features are handled by applying appropriate machine learning algorithms to extract the relationships between the potential transaction and the previous ones. Experiments based on real data sets show that our approach is more accurate than other trust mechanisms, especially when the information about past behavior of the specific agent is rare, incomplete, or inaccurate.

Abstract: Noncryptographic hash functions have an immense number of important practical applications owing to their powerful search properties. However, those properties critically depend on good designs: Inappropriately chosen hash functions are a very common source of performance losses. On the other hand, hash functions are difficult to design: They are extremely nonlinear and counterintuitive, and relationships between the variables are often intricate and obscure. In this work, we demonstrate the utility of genetic programming GP and avalanche effect to automatically generate noncryptographic hashes that can compete with state-of-the-art hash functions. We describe the design and implementation of our system, called GP-hash, and its fitness function, based on avalanche properties. Also, we experimentally identify good terminal and function sets and parameters for this task, providing interesting information for future research in this topic. Using GP-hash, we were able to generate two different families of noncryptographic hashes. These hashes are able to compete with a selection of the most important functions of the hashing literature, most of them widely used in the industry and created by world-class hashing experts with years of experience.

Abstract: This paper discusses inter-robot and human-robot communication by bare hand dynamic gestures. We use a Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. The K-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF does not track the order of events. To counter the unordered events of the BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on the newly collected complex dataset show a higher level of recognition. We used the same above mentioned approach for inter-robot communication by using two sample hand models.

Abstract: Until recently use of sensors to collect sensitive parameters had only few risk factors such as sensor malfunction, uncertainty of data collection, or missing data coverage. Some issues in these categories were addressed by multi-sensor application or sensor fusion. However, advancement in technology and advent of wireless sensors in a networked environment, brought along a new risk factor related to the security in wireless sensor network. Therefore security in Wireless Sensor Network WSNs is challenging and critical to the functionality of the networked sensors. This is very important in cases of highly secure environment, especially in industrial, military, and medical domains. The standard WSN protocols focus on energy efficiency; transmission efficiency, and routing. WSN is known for limitations on hardware and software and for resources-constrained in general. An adaptive model of security that meets requirements and constraints in WSN is Intrusion Detections. This article investigates security in WSN and presents a design process for achieving optimum security based on requirements and constraints in WSNs. Further, comparative results between a proposed technique and other security current approaches are discussed.

Abstract: The paper proposes a framework for in-home physical exercise monitoring based on a Kinect platform. The analysis goes beyond the state-of-the-art solutions by monitoring more joints and offering more advanced reporting capabilities on the movement such as: the position and trajectory of each joint, the working envelope of each body member, the average velocity, and a measure of the user's fatigue after an exercise sequence. This data can be visualised and compared to a standard (e.g. a healthy user, for rehabilitation purposes) or an ideal performance (e.g. a perfect sport pose for exercising) in order to give the user a measure on his/her own performance and incite his/her motivation to continue the training program. Such information can be used as well by a therapist or professional sports trainer to evaluate the progress of a patient or of a trainee.

Abstract: Most real-world negotiation involves multiple interdependent issues, which makes an agent's utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they cannot find a high-quality solution when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the utility space into several largely independent subspaces. In this paper, we propose a method for decomposing a utility space based on every agent's utility space. In addition, the mediator finds the contracts in each group based on the votes from all agents, and combines the contract in each issue-group. This method allows good outcomes with greater scalability than the method without issue-grouping. We demonstrate that our protocol, based on issue-groups, has a higher optimality rate than previous efforts, and discuss the impact on the optimality of the negotiation outcomes.

Abstract: This article addresses reinforcement learning problems based on factored Markov decision processes MDPs in which the agent must choose among a set of candidate abstractions, each build up from a different combination of state components. We present and evaluate a new approach that can perform effective abstraction selection that is more resource-efficient and/or more general than existing approaches. The core of the approach is to make selection of an abstraction part of the learning agent's decision-making process by augmenting the agent's action space with internal actions that select the abstraction it uses. We prove that under certain conditions this approach results in a derived MDP whose solution yields both the optimal abstraction for the original MDP and the optimal policy under that abstraction. We examine our approach in three domains of increasing complexity: contextual bandit problems, episodic MDPs, and general MDPs with context-specific structure. © 2013 Wiley Periodicals, Inc.

Abstract: Chlorophyll fluorescence (ChlF), a plant response in time to stressors, has long been known to be a useful tool to detect plant stress. Early and accurate plant stress detection is imperative in enabling timely and appropriate intervention. One major limitation of prior work is that, in general, only a few key inflection points of a localized section of a chlorophyll fluorescence signal are used to calculate single index values. These values yield very limited insight into stress level or type. In this paper, we present a method for plant stress classification that uses global (versus local) ChlF time-varying signal data acquired via imaging. We classify this time-varying-intensity-signal using a Hidden Markov Model (HMM). While HMMs have been used in other fields, in this paper we present their first application in the field of plant stress clustering and classification. We show how the proposed selection of a low-pass filtered plant's entire chlorophyll fluorescence signal profile, as a global feature selection, improves the accuracy of plant stress classification. Additionally, we show how the rate of change-in-time of the plant ChlF intensity time-varying profiles further improves the plant stress classification accuracy. Finally, we present experimental results to show the value and potential of the proposed method to enable more accurate and specific classification of plant stressor levels and stressor types.

Abstract: This paper overviews the capabilities offered by the smartphones as advanced computing devices for healthcare applications, focusing on the prevention of short-term complications of chronic obtrusive pulmonary disease COPD patients, using personalised decision making. Data provided by the embedded smartphone sensors, wearable wireless body area networks and internet of things objects are incorporated in a framework that evaluates and alerts the COPD patient for potentially risky environmental conditions in the proximal area. Data processing schemas are presented, distributing the execution of the calculations between the smartphone and a cloud-hosted service, achieving the ideal equilibrium between processing speed, system scalability and battery life of the mobile devices.

Abstract: Classification of electroencephalographic (EEG) signals is a sophisticated task that determines the accuracy of thought pattern recognition performed by computer-brain interface (BCI) which, in turn, determines the degree of naturalness of the interaction provided by that system. However, classifying the EEG signals is not a trivial task due to their non-stationary characteristics. In this paper, we introduce and utilize incremental quantum particle swarm optimization (IQPSO) algorithm for incremental classification of EEG data stream. IQPSO builds the classification model as a set of explicit rules which benefits from semantic symbolic knowledge representation and enhanced comprehensibility. We compared the performance of IQPSO against ten other classifiers on two EEG datasets. The results suggest that IQPSO outperforms other classifiers in terms of classification accuracy, precision and recall.

Abstract: Fingerprint matching techniques have been extensively investigated in the literature. Minutiae-based and image-based approaches are two major classes of fingerprint matching. Minutiae-based approaches are still not able to achieve high matching accuracy, particularly in low quality images. Now-a-days, image-based methods are given more attention as they extract more discriminatory information in comparison to minutiae-based methods. In this paper, we have proposed an image-based method, which exploits the rotation invariant local directional fixed length descriptors. This method involves preprocessing, region of interest (ROI) computation and extraction of proposed local directional fixed length descriptors. The proposed descriptors are tested on public benchmark databases FVC2002 and FVC2004 using Euclidian distance, Chi-square distance, histogram intersection, and least square support vector machine (LS-SVM). The effectiveness of the proposed method is demonstrated by comparing the experimental results with those of other image-based approaches proposed in the literature.

Abstract: There is an increasing interest in complex automated negotiations, where agents negotiate about multiple, interdependent issues and agent utility functions exhibit low autocorrelation. In these scenarios, the negotiation mechanisms used to find agreement solutions among agents tend to fail due to the complexity of agents' preference spaces, and this tendency increases as the degree of autocorrelation decreases. In this paper, we propose an automated negotiation model specially tailored for highly uncorrelated utility spaces based on weighted constraints. The model relies on a mediated, auction-based interaction protocol and a set of heuristic mechanisms for bidding and deal identification. To address the challenges raised by highly uncorrelated utility spaces, we propose to use a quality factor, which allows agents to balance utility and deal probability when placing their bids or when searching for agreement regions among these bids. Experiments show that the proposed negotiation model achieves high optimality results and low failure rates even in negotiation scenarios involving highly uncorrelated utility spaces, thus outperforming previous approaches.

Abstract: A speaker invariant speech recognition system is proposed by analysing the characteristics of speech signal. The distinctive features are derived from the speech data using discrete wavelet transforms (DWT) and human factor cepestral coefficient (HFCC) technique. This HFCC technique provides an immense impact on signal decoupling process for adjusting parameters in noise smoothing and spectral resolution. We have created a speech repository of 12 isolated Hindi words. The principal component analysis (PCA) is applied on speech features obtained from HFCC analysis in order to reduce the dimension of feature space. We have applied Bayes' decision rule for classification with multivariate normal distribution which follows the class conditional probability density function for each training classes. The performance of the classifier has been evaluated by calculating the misclassification error probability. Experimental results of proposed method are analysed and compared with the existing methods like MFCC with DWT, MFCC with PCA, DWT with PCA, etc. We have achieved promising classification results using HFCC-based speech features for speaker invariant speech identification system.

Abstract: Metaheuristic techniques are being successfully used as optimisation methods in various application areas. This paper presents modification in one such metaheuristic called as harmony search HS that is inspired from music improvisation process. The two parameters, harmony memory consideration rate HMCR and pitch adjusting rate PAR, in HS play important role in improvisation of new harmony. Instead of keeping the parameters fixed, as reported in many existing algorithms, these are being allowed to change dynamically during the process of improvisation in the proposed algorithm. This paper further examines the effect on the results when K-means is initialised with solution returned by the proposed algorithm. The effect of harmony memory size has also been investigated on proposed approach. The experiments are performed for data clustering on nine benchmark datasets. The clustering performance of proposed algorithm is compared with K-means, fuzzy C-means, genetic algorithm, and four recently proposed variants of HS. The results are encouraging and demonstrate that the proposed algorithm provides much better values in terms of precision, recall, G-measure, inter-cluster and intra-cluster distances.

Abstract: Typically, massive rotator cuff tears have stiff and retracted tendon with poor muscle quality, in such cases orthopaedic surgeons are confronted with big challenging to restore the cuff to its native footprint. Furthermore, even with some restoration of the footprint, it is related with a high re-tear rate due to less tension free repair and less tendon coverage. In this tough circumstance, the partial repair has yielded satisfactory outcomes at relatively short follow-up by re-creating the transverse force couple of the rotator cuff. Through this partial repair, the massive rotator cuff tear can be converted to the “functional rotator cuff tear” and provide improvement in pain and functional outcomes in patient’s shoulder.(Clin Shoulder Elb 2014;17(1):44-47)Key Words: Shoulder; Massive rotator cuff tear; Partial repair; Arthroscopy

Abstract: Flash memory's ability to be programmed multiple times is called its endurance. Beyond being able to give more accurate chip specifications, more precise knowledge of endurance would permit manufacturers to use flash chips more effectively. Rather than physical testing to determine chip endurance, which is impractical because it takes days and destroys an area of the chip under test, this research seeks to predict whether chips will meet chosen endurance criteria. Timing data relating to erasure and programming operations is gathered as the basis for modelling. The purpose of this paper is to determine which methods can be used on this data to accurately and efficiently predict endurance. Traditional statistical classification methods, support vector machines and genetic programming are compared. Cross-validating on common datasets, the classification methods are evaluated for applicability, accuracy and efficiency and their respective advantages and disadvantages are quantified.

Abstract: This paper deals with the optimal tuning of proportional-integral-derivative (PID) controllers for a pancake direct current (DC) torque motor system that belongs to a Diesel engine exhaust gas recirculation valve in automotive applications. The Bacterial Foraging Optimization (BFO) algorithms solve an optimization problem which targets the minimization of an objective function expressed as the weighted sum of overshoot plus the integral of squared control error, and the parameters of the PID controllers are the variables of the objective function. Our BFO algorithms are characterized by the validation of the position of bacteria only if the PID control system response is in a valid range. A digitally simulated case study which deals with the shaft angle control of a DC torque motor system is considered. The impact of four parameters of one BFO algorithm on the objective function values is discussed.

Abstract: In this article, we propose a novel super-agent-based framework for reputation management and community formation in decentralized systems. We describe this framework in the context of Web service selection where agents with more capabilities act as super-agents. These super-agents serve as reputation managers to maintain reputation information of services and share the information with other consumer agents that have fewer capabilities than the super-agents. In addition, super-agents can maintain communities and build community-based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria as the super-agents or the other community members. A practical reward mechanism is also introduced to create incentives for super-agents to contribute their resources to maintain reputation and form communities and provide truthful reputation information. Experimental results obtained through simulation confirm that our approach achieves better effectiveness and scalability compared to the systems that do not use super-agents and that do not form communities.

Abstract: Lateral epicondylitis is one of the most common causes of elbow pain and has been known to be caused by degeneration of the extensor carpi radialis brevis (ECRB). Nonoperative treatment should be tried first in all patients, because it has been deemed highly successful; however only few prospective studies suggest that symptoms frequently was completely resolved. Operative treatment is indicated for recalcitrant pain after failed conservative treatment, which involves excision of the pathologic portion of the ECRB and results in a high degree of subjective relief and functional restoration. We will review the pathology of the lateral epicondylitis and operative and nonoperative treatment of lateral epicondylitis.

Abstract: One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer-oriented and product-oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real-world data set are performed to demonstrate the effectiveness of the proposed framework.

Abstract: Osteochondritis dissecans (OCD) is an idiopathic condition affecting the articular epiphysis Initially described in the knee, this entity af-fects several other parts of the body such as the talar dome, tarsal navicular, and femoral capital epiphysis Osteochondritis dissecans (OCD) of the elbow is typically located in the capitellum of the humerus in young teenagers OCD of humeral trochlea is very rare, but can be occurred among young athletes OCD developed medial trochlea was extremely rare, especially, without any other trauma We present a patient, pitcher with OCD in the medial trochlea of the humerus who underwent arthroscopic debridement and microfracture(Clin Shoulder Elb 2014;17(1):40-43)Key Words: Osteochondritis dissecans; Pitcher; Medial trochlea; Arthroscopic debridement; Microfracture

Abstract: Software faults are recoverable errors in a program that occur due to the programming errors. Software fault prediction is subject to problems like non-availability of fault data which makes the application of supervised technique difficult. In such cases, unsupervised techniques are helpful. In this paper, a hyper-quadtree-based K-means algorithm has been applied for predicting the faults in the program module. This paper contains two parts. First, the hyper-quadtree is applied on the software fault prediction dataset for the initialization of the K-means clustering algorithm. An input parameter Δ governs the initial number of clusters and cluster centers. Second, the cluster centers and the number of cluster centers obtained from the initialization algorithm are used as the input for the K-means clustering algorithm for predicting the faults in the software modules. The overall error rate of this prediction approach is compared with the other existing algorithms.

Abstract: In the patients of retracted massive rotator cuff tears, there are much of difficulty to functional recovery and pain relief Nevertheless the development of treatment, there are still debates of the best treatments in the massive rotator cuff tears Recenlty various of treatments are introduced; these are acromioplasty with debridement, biceps tenotomy, great tuberoplasty with biceps tenotomy, partial repair, mini-open rotator cuff repair, arthroscopic rotator cuff repair, soft tissue augmentation, tendon transfer, flap, hemiarthroplasty, and reverse total shoulder arthroplasty That there is no difference of result for reverse total shoulder arthroplasty between patients who have massive rotator cuff tear without arthritis and patients who have cuff tear arthropathy Reverse total shoulder arthroplasty is one of reliable and successful treatment options for massive rotator cuff tear Especially it is more effective for patients who have a pseudoparalysis

Abstract: An important task in biological information extraction is to identify descriptions of biological relations and events involving genes or proteins. In this work, we propose a graph-based approach to automatically learn rules for detecting biological events in the life science literature. The event rules are learned by identifying the key contextual dependencies from full parsing of annotated text. The detection is performed by searching for isomorphism between event rules and the dependency graphs of complete sentences. When applying our approach to the data sets of the Task1 of the BioNLP-ST 2009, we achieved a 40.71% F-score in detecting biological events across nine event types. Our 56.32% precision is comparable with the state-of-the-art systems. The approach may also be generalized to extract events from other domains where training data are available because it requires neither manual intervention nor external domain-specific resources. The subgraph matching algorithm we developed is released under the new BSD license and can be downloaded from http://esmalgorithm.sourceforge.net.

Abstract: Recurrent posterior shoulder instability is a debilitating condition that is relatively uncommon, but its diagnosis in young adults is increasing in frequency. Several predisposing factors for this condition have been identified, such as the presence of an abnormal joint surface orientation, an osteochondral fracture of the humeral head or glenoid cavity, and a postero-inferior capsuloligamentary deficit, but their relative importance remains poorly understood. Whilst, conservative treatment is effective in cases of hyperlaxity or in the absence of bone abnormality, failure of conservative treatment means that open or arthroscopic surgery is required. In general, soft-tissue reconstructions are carried out in cases of capsulolabral lesions in which bone anatomy is normal, whereas bone grafts have been required in cases where posterior bony Bankart lesions, glenoid defects, or posterior glenoid dysplasia are present. However, a consensus on the exact management of posterior shoulder instability is yet to be reached, and published studies are few with weak evidence. In our study, we report the reconstruction of the glenoid using iliac bone graft in a patient suffering recurrent posterior shoulder instability with severe glenoid bone defect.

Abstract: A ganglion is a benign cystic mass, commonly found around a joint or tendon sheath. It frequently occurs at the wrist, foot, ankle, and knee. Intratendinous ganglion has been rarely reported, and intratendinous ganglion of the long head of biceps brachii is extremely rare. According to our literature review, this is the third case of intratendinous ganglion of the long head of biceps brachii, and the first case of arthroscopic treatment. Therefore we report a case of an arthroscopic treatement for an intratendinous ganglion of the long head of biceps brachii. (Clin Shoulder Elbow 2014;17(4):194-196)

Abstract: Efficient development of software requires accurate estimates. It is unlikely to expect very accurate estimates of software development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. In this study, two analogy methods based on integration of Grey Relational Effort Analysis Technique using Robust Regression Methods with and without feature subset selection have been proposed. In the previous, Grey Relational based effort estimation studies, GRA is used to assess similarity between projects with m features and effort is estimated from k most similar projects. In the proposed methodologies, the effort of the reference project is estimated by applying regression techniques to k most similar projects obtained using GRA as the similarity metric. Empirical results obtained are statistically significant, indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

Abstract: Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re-identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re-identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter two popular social networks based on matching pseudonyms. We used Bayesian networks to model the re-identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7-11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re-identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re-identify users with a sensitivity of 41% and specificity of 96%. The potential for re-identification increases as more data is accumulated from these profiles, which makes the threat of re-identification more serious.