Genital trauma and vaginal bleeding: is it a lapse of time issue? A case report of a prepubertal girl and review of the literature

Child victims of sexual abuse may present with physical findings whose interpretation requires the most exhaustive evaluation and an accurate collection of a detailed history. Genital bleeding is usually considered as an acute sign, related to a trauma that occurred shortly before its appearance. We report a case of a 34-month-old child who was referred to the emergency room with a significant vaginal hemorrhage, originating from a wide laceration of the posterior fourchette, and a negative history for accidental trauma. The characteristics of the lesion, compared to the temporal evolution of the healing process, and the witnesses’ depositions led us to assume that the time elapsed between the abusive event and the physical examination was longer in respect to what had appeared at the first sight. The judicial reconstruction of the events confirmed our assumption, allowing the charge of the right abuse perpetrator. As the literature regarding this eventuality is very poor, we report this case to stress the importance for physicians to consider that an active bleeding may be the manifestation of a trauma that occurred very long before.


Novel Compact Tri-Band Bandpass Filter With Controllable Bandwidths

Novel compact tri-band bandpass filter (BPF) with controllable bandwidths is presented in this letter. The tri-band performance is realized by using three sets of resonators, i.e., two sets of short-stub loaded dual-mode resonators, which are designed for the first and second passband, respectively, and half-wavelength resonators for the third passband. The three passband frequencies and bandwidths could be independently tuned and designed. The half-wavelength resonators are embedded between the two dual-mode resonators and serve as part of feed lines for the first and second passband, thus compact size is realized. The newly designed tri-band BPF is verified by circuit implementation and good agreement between the simulated and measured results can be observed.


Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging

We present a system to assist users in dietary logging habits, which performs food recognition from pictures snapped on their phone in two different scenarios. In the first scenario, called "Food in context", we exploit the GPS information of a user to determine which restaurant they are having a meal at, therefore restricting the categories to recognize to the set of items in the menu. Such context allows us to also report precise calories information to the user about their meal, since restaurant chains tend to standardize portions and provide the dietary information of each meal. In the second scenario, called "Foods in the wild" we try to recognize a cooked meal from a picture which could be snapped anywhere. We perform extensive experiments on food recognition on both scenarios, demonstrating the feasibility of our approach at scale, on a newly introduced dataset with 105K images for 500 food categories.


A Tandem Algorithm for Singing Pitch Extraction and Voice Separation From Music Accompaniment

Singing pitch estimation and singing voice separation are challenging due to the presence of music accompaniments that are often nonstationary and harmonic. Inspired by computational auditory scene analysis (CASA), this paper investigates a tandem algorithm that estimates the singing pitch and separates the singing voice jointly and iteratively. Rough pitches are first estimated and then used to separate the target singer by considering harmonicity and temporal continuity. The separated singing voice and estimated pitches are used to improve each other iteratively. To enhance the performance of the tandem algorithm for dealing with musical recordings, we propose a trend estimation algorithm to detect the pitch ranges of a singing voice in each time frame. The detected trend substantially reduces the difficulty of singing pitch detection by removing a large number of wrong pitch candidates either produced by musical instruments or the overtones of the singing voice. Systematic evaluation shows that the tandem algorithm outperforms previous systems for pitch extraction and singing voice separation.


A spaceborne telemetry loaded bifilar helical antenna for LEO satellites

In this paper we describe the design, manufacturing and measurement of an antenna with an isoflux type radiation pattern used in the space segment TT&C subsystem. In particular, the antenna corresponds to the flight model of mission SAC-D/AQUARIUS, which is a LEO earth observation satellite. The chosen solution is a loaded bifilar helical antenna, which presents a higher gain than a typical hemispherical antenna but which doesn't require the use of a beam forming network like the classical quadrifilar Kilgus type antenna. This reduces the number of required parts, and due to its loading, smaller dimension and higher structural stiffness are achieved.


Fast algorithms for mining association rules


Sampling Large Databases for Association Rules

Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a random sample, to nd using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and in those rare cases where our sampling method does not produce all association rules, the missing rules can be found in a second pass. Our experiments show that the proposed algorithms can nd association rules very e ciently in only one database pass.


Mining Quantitative Association Rules in Large Relational Tables

We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fine-partitioning the values of the attribute and then combining adjacent partitions as necessary. We introduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a "greater-than-expected-value" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.


Rethinking Education in the Age of Technology

By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more trusted. As this rethinking education in the age of technology review, it will really give you the good idea to be successful. It is not only for you to be success in certain life you can be successful in everything. The success can be started by knowing the basic knowledge and do actions.


Pathophysiological distortions in time perception and timed performance.

Distortions in time perception and timed performance are presented by a number of different neurological and psychiatric conditions (e.g. Parkinson's disease, schizophrenia, attention deficit hyperactivity disorder and autism). As a consequence, the primary focus of this review is on factors that define or produce systematic changes in the attention, clock, memory and decision stages of temporal processing as originally defined by Scalar Expectancy Theory. These findings are used to evaluate the Striatal Beat Frequency Theory, which is a neurobiological model of interval timing based upon the coincidence detection of oscillatory processes in corticostriatal circuits that can be mapped onto the stages of information processing proposed by Scalar Timing Theory.


User Traffic Profile for Traffic Reduction and Effective Bot C&C Detection

Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.


PreCog: Improving Crowdsourced Data Quality Before Acquisition

Quality control in crowdsourcing systems is crucial. It is typically done after data collection, often using additional crowdsourced tasks to assess and improve the quality. These post-hoc methods can easily add cost and latency to the acquisition process—particularly if collecting high-quality data is important. In this paper, we argue for pre-hoc interface optimizations based on feedback that helps workers improve data quality before it is submitted and is well suited to complement post-hoc techniques. We propose the Precog system that explicitly supports such interface optimizations for common integrity constraints as well as more ambiguous text acquisition tasks where quality is ill-defined. We then develop the Segment-Predict-Explain pattern for detecting low-quality text segments and generating prescriptive explanations to help the worker improve their text input. Our unique combination of segmentation and prescriptive explanation are necessary for Precog to collect 2× more high-quality text data than non-Precog approaches on two real domains.


ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking

We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.


Nonspeech oral motor treatment issues related to children with developmental speech sound disorders.

PURPOSE
This article examines nonspeech oral motor treatments (NSOMTs) in the population of clients with developmental speech sound disorders. NSOMTs are a collection of nonspeech methods and procedures that claim to influence tongue, lip, and jaw resting postures; increase strength; improve muscle tone; facilitate range of motion; and develop muscle control. In the case of developmental speech sound disorders, NSOMTs are employed before or simultaneous with actual speech production treatment.

METHOD
First, NSOMTs are defined for the reader, and there is a discussion of NSOMTs under the categories of active muscle exercise, passive muscle exercise, and sensory stimulation. Second, different theories underlying NSOMTs along with the implications of the theories are discussed. Finally, a review of pertinent investigations is presented.

RESULTS
The application of NSOMTs is questionable due to a number of reservations that include (a) the implied cause of developmental speech sound disorders, (b) neurophysiologic differences between the limbs and oral musculature, (c) the development of new theories of movement and movement control, and (d) the paucity of research literature concerning NSOMTs.

CLINICAL IMPLICATION
There is no substantive evidence to support NSOMTs as interventions for children with developmental speech sound disorders.


Precise Flow-Insensitive May-Alias Analysis is NP-Hard

Determining aliases is one of the foundamental static analysis problems, in part because the precision with which this problem is solved can affect the precision of other analyses such as live variables, available expressions, and constant propagation. Previous work has investigated the complexity of flow-sensitive alias analysis. In this article we show that precise flow-insensitive may-alias analysis is NP-hard given arbitrary levels of pointers and arbitrary pointer dereferencing.


Automated Generation of Event-Oriented Exploits in Android Hybrid Apps

Recently more and more Android apps integrate the embedded browser, known as “WebView”, to render web pages and run JavaScript code without leaving these apps. WebView provides a powerful feature that allows event handlers defined in the native context (i.e., Java in Android) to handle web events that occur in WebView. However, as shown in prior work, this feature suffers from remote attacks, which we generalize as EventOriented Exploit (EOE) in this paper, such that adversaries may remotely access local critical functionalities through event handlers in WebView without any permission or authentication. In this paper, we propose a novel approach, EOEDroid, which can automatically vet event handlers in a given hybrid app using selective symbolic execution and static analysis. If a vulnerability is found, EOEDroid also automatically generates exploit code to help developers and analysts verify the vulnerability. To support exploit code generation, we also systematically study web events, event handlers and their trigger constraints. We evaluated our approach on 3,652 most popular apps. The result showed that our approach found 97 total vulnerabilities in 58 apps, including 2 cross-frame DOM manipulation, 53 phishing, 30 sensitive information leakage, 1 local resources access, and 11 Intent abuse vulnerabilities. We also found a potential backdoor in a high profile app that could be used to steal users’ sensitive information, such as IMEI. Even though developers attempted to close it, EOEDroid found that adversaries were still able to exploit it by triggering two events together and feeding event handlers with well designed input.


Encountering stronger password requirements: user attitudes and behaviors

Text-based passwords are still the most commonly used authentication mechanism in information systems. We took advantage of a unique opportunity presented by a significant change in the Carnegie Mellon University (CMU) computing services password policy that required users to change their passwords. Through our survey of 470 CMU computer users, we collected data about behaviors and practices related to the use and creation of passwords. We also captured users' opinions about the new, stronger policy requirements. Our analysis shows that, although most of the users were annoyed by the need to create a complex password, they believe that they are now more secure. Furthermore, we perform an entropy analysis and discuss how our findings relate to NIST recommendations for creating a password policy. We also examine how users answer specific questions related to their passwords. Our results can be helpful in designing better password policies that consider not only technical aspects of specific policy rules, but also users' behavior in response to those rules.


A multiview 3D modeling system based on stereo vision techniques

This paper introduces a stereo vision system to automatically generate 3D models of real objects. 3D model generation is based on the merging of multiview range images obtained from a digital stereo camera. Stereo images obtained from the camera are rectified, and a correlation-based stereo matching technique reconstructs range images from them. A turntable stage is also employed to obtain multiple range images of the objects. To register range images into a common coordinate system automatically, we introduce and calibrate a turntable coordinate system with respect to the camera coordinate system. After the registration of multiview range images, a 3D model is reconstructed using a volumetric integration technique. Error analysis on turntable calibration and 3D model reconstruction shows the accuracy of our 3D modeling system.


On the compensation of dead time and zero-current crossing for a PWM-inverter-controlled AC servo drive

Conventional dead-time compensation methods, for pulsewidth-modulated (PWM) inverters, improve the current output waveforms; however, the zero-current crossing effect is still apparent. This letter proposes a new method, based on angle domain repetitive control, to reduce the distortions in the PWM inverters output waveforms caused by the dead time and the zero-crossing problem.


Design 13.56MHz 10 kW resonant inverter using GaN HEMT for wireless power transfer systems

This paper presents the design process of a 13.56 MHz 10 kW and high efficiency inverter for wireless power transfer systems. The new 650V E-mode GaN is used in multiphase resonant topology to obtain high output power. The optimized efficiency design and the driver design are presented in detail. The circuit simulation results show that the inverter achieves 10 kW output power with the efficiency of over 98%. The experiment system is tested at 4 kW output power with the efficiency of 96.5%.


Correspondence-free pose estimation for 3D objects from noisy depth data

Estimating the pose of objects from depth data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from noisy 2.5D depth images obtained from a consumer depth sensor. Initialized with a coarsely accurate pose, the proposed approach applies a hypothesize-and-test scheme that combines stochastic optimization and graphics-based rendering to refine the supplied initial pose, so that it accurately accounts for a sensed depth image. Pose refinement employs particle swarm optimization to minimize an objective function that quantifies the misalignment between the acquired depth image and a rendered one that is synthesized from a hypothesized pose with the aid of an object mesh model. No explicit correspondences between the depth data and the model need to be established, whereas pose hypothesis rendering and objective function evaluation are efficiently performed on the GPU. Extensive experimental results demonstrate the superior performance of the proposed approach compared to the ICP algorithm, which is typically used for pose refinement in depth images. Furthermore, the experiments indicate the graceful degradation of its performance to limited computational resources and its robustness to noisy and reduced polygon count models, attesting its suitability for use with automatically scanned object models and common graphics hardware.


Techniques for the Automated Analysis of Musical Audio

This thesis presents work on automated analysis techniques for musical audio. A complete analysis of the content within an audio waveform is termed ‘transcription’ and can be used for intelligent, informed processing of the signal in a variety of applications. However, full transcription is beyond the ability of current methods and hence this thesis concerns subtasks of whole problem. A major part of musical analysis is the extraction of signal information from a time-frequency representation, often the short time Fourier transform or spectrogram. The ‘reassignment’ method is a technique for improving the clarity of these representations. It is comprehensively reviewed and its relationship to a number of classical instantaneous frequency measures is explicitly stated. Performance of reassignment as a sinusoidal frequency estimator is then assessed quantitatively for the first time. This is achieved in the framework of the Cramér-Rao bound. Reassignment is shown to be inferior to some other estimators for simple synthetic signals but achieves comparable results for real musical examples. Several extensions and uses of the reassignment method are then described. These include the derivation of reassignment measures extracted from the amplitude of the spectrogram, rather than the traditional phase-based method and the use of reassignment measures for signal classification. The second major area of musical analysis investigated in this thesis is beat tracking, where the aim is to extract both the tempo implied in the music and also its phase which is analagous to beat location. Three models for the beat in general music examples are described. These are cast in a state-space formulation within the Bayesian paradigm, which allows the use of Monte Carlo methods for inference of the model parameters. The first two models use pre-extracted note onsets and model tempo as either a linear Gaussian process or as Brownian motion. The third model also includes on-line detection of onsets, thus adding an extra layer of complexity. Sequential Monte Carlo algorithms, termed particle filters, are then used for the estimation of the data. The systems are tested on an extensive database, nearly three and a half hours in length and consisting of various styles of music. The results exceed the current published state of the art. The research presented here could form the early stages of a full transcription system, a proposal for which is also expounded. This would use a flow of contextual information from simpler, more global structures to aid the inference of more complex and detailed processes. The global structures present in the music (such as style, structure, tempo, etc.) still have their own uses, making this scheme instantly applicable to real problems.


A user's guide to tabu search

We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.


A simple hybrid 3-level buck-boost DC-DC converter with efficient PWM regulation scheme

This paper describes a 3-level buck-boost converter with an output voltage range up to double the input voltage. The proposed converter can be used for applications that require stepping up/down the input voltage depending on the operating conditions like solar power harvesting. The converter has a hybrid structure between inductor-based converters and capacitor-based converters by utilizing a one flying capacitor and an inductor in the charge transfer process. The converter features smaller inductor size and current ripples as compared to the traditional buck-boost inductor-based converter while offering a high efficiency as compared to SC converters making it suitable for efficient on-chip integration. A prototype circuit has been designed on a 65nm technology using a 5-nH parasitic bondwire inductor. Simulation results show that the proposed converter features up to 20% improvement in the efficiency over a conventional 1:2 SC converter for a wide range of output voltage values.


Machine Learning and Inference Laboratory An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model

Optimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For this reason, more intelligent search techniques are needed. This paper presents and evaluates a novel optimization system, called ISHED1 (Intelligent System for Heat Exchanger Design). This system uses a recently developed non-Darwinian evolutionary computation method to seek evaporator circuit designs that maximize the capacity of the evaporator under given technical and environmental constraints. Circuitries were developed for an evaporator with three depth rows of 12 tubes each, based on optimizing the performance with uniform and non-uniform airflow profiles. ISHED1 demonstrated the capability to design an optimized circuitry for a non-uniform air distribution so the capacity showed no degradation over the traditional balanced circuitry design working with a uniform airflow.


Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images

Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their interpretation is time consuming and subjective, even for trained dermatologists. The current computer-aided diagnosis (CAD) systems are mainly noninteractive in nature and their prediction represents just a cue for the dermatologist, as the final decision regarding the likelihood of the presence of a melanoma is left exclusively to him/her. Recently, developing CAD schemes that use image retrieval approach to search for the clinically relevant and visually similar lesions has been attracting research interest. Although preliminary studies have suggested that using retrieval might improve dermatologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage with lack of benchmark evaluation in ground-truth datasets to compare retrieval effectiveness. A CAD system based on both classification and retrieval would be more effective and robust. This work is focusing on by addressing the various issues related to the development of such an integrated and interactive CAD system by performing automatic lesion segmentation with an adaptive thresholding and region growing approach, extracting invariant features from lesions and classifying and retrieving those using Extreme Learning Machines (ELM) and a similarity fusion approach. Finally, various methods are evaluated with promising results in a benchmark dataset of International Skin Imaging Collaboration (ISIC).


A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.


Perception on the Usage of Mobile Assisted Language Learning ( MALL ) in English as a Second Language ( ESL ) Learning among Vocational College Students

Mobile phones are viewed as significant aids to English language learning. Numerous studies have been conducted in different contexts on mobile assisted language learning (MALL) that indicate the benefits of mobile devices for English language learning. Nevertheless, few studies on MALL had been conducted on among vocational college students in Malaysia. Therefore, this study was aimed to investigate the perception on the usage of MALL in English as a Second Language (ESL) among private vocational college students. Data were collected from a survey questionnaire adapted from Technology Acceptance Model (TAM). The results show the respondents have positive perception on the usage of MALL. Majority of the respondents showed overall agreement on both constructs perceived usefulness (PU) and perceived ease of use (PEoU) of MALL. They believed the usage of MALL will enhance the teaching and learning process. This evidence of acceptance has implication for educators and curriculum designers to exploit the mobile phone for autonomous and interactive ESL learning beyond the classroom context. It is hoped that MALL will be used as one of the teaching aids that could assist educators to teach English as a Second Language (ESL) more effectively.


A probabilistic fuzzy logic system for modeling and control

In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.


CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services” to endusers under a usage-based payment model. They can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behaviour modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited efforts. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organisations such as HP Labs in USA are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.


IT Security Investments Through the Lens of the Resource-Based View: A new Theoretical Model and Literature Review

IT security has become a major issue for organizations as they need to protect their assets, including IT resources, intellectual property and business processes, against security attacks. Disruptions of ITbased business activities can easily lead to economic damage, such as loss of productivity, revenue and


CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets

Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an e cient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single pre x path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is e cient and scalable over large databases, and is faster than the previously proposed methods.


A comparison of approaches to large-scale data analysis

There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures.


Starfish: A Self-tuning System for Big Data Analytics

Hadoop is a MAD system for data analytics  Magnetism: attracts all sources of data  Agility: adapts in sync with rapid data evolution  Depth: supports complex analytics needs Starfish makes Hadoop MADDER and Self-Tuning  Data-lifecycle-awareness: achieves good performance throughout data lifecycle  Elasticity: adjusts resources and operational costs  Robustness: provides availability and predictability Just-in-Time Job Optimization


A Language Independent Algorithm for Single and Multiple Document Summarization

S S S w S w w S S Sim + ∈ ∧ ∈ = other similarity metrics: cosine, string kernels, etc. 3. r i BC-HurricaneGilbert 09-11 0339 4. BC-Hurricane Gilbert , 0348 5. Hurricane Gilbert Heads Toward Dominican Coast 6. By RUDDY GONZALEZ 7. Associated Press Writer 8. SANTO DOMINGO , Dominican Republic ( AP ) 9. Hurricane Gilbert swept toward the Dominican Republic Sunday , and the Civil Defense alerted its heavily populated south coast to prepare for high winds , heavy rains and high seas . 10. The storm was approaching from the southeast with sustained winds of 75 mph gusting to 92 mph . 11. " There is no need for alarm , " Civil Defense Director Eugenio Cabral said in a television alert shortly before midnight Saturday . 12. Cabral said residents of the province of Barahona should closely follow Gilbert 's movement . 13. An estimated 100,000 people live in the province , including 70,000 in the city of Barahona , about 125 miles west of Santo Domingo . 14. Tropical Storm Gilbert formed in the eastern Caribbean and strengthened into a hurricane Saturday night . 15. The National Hurricane Center in Miami reported its position at 2a.m. Sunday at latitude 16.1 north , longitude 67.5 west , about 140 miles south of Ponce , Puerto Rico , and 200 miles southeast of Santo Domingo . 16. The National Weather Service in San Juan , Puerto Rico , said Gilbert was moving westward at 15 mph with a " broad area of cloudiness and heavy weather " rotating around the center of the storm . 17. The weather service issued a flash flood watch for Puerto Rico and the Virgin Islands until at least 6p.m. Sunday . 18. Strong winds associated with the Gilbert brought coastal flooding , strong southeast winds and up to 12 feet feet to Puerto Rico 's south coast . 19. There were no reports of casualties . 20. San Juan , on the north coast , had heavy rains and gusts Saturday , but they subsided during the night . 21. On Saturday , Hurricane Florence was downgraded to a tropical storm and its remnants pushed inland from theU.S. Gulf Coast . 22. Residents returned home , happy to find little damage from 80 mph winds and sheets of rain . 23. Florence , the sixth named storm of the 1988 Atlantic storm season , was the second hurricane . 24. The first , Debby , reached minimal hurricane strength briefly before hitting the Mexican coast last month . 4 6 5


The Minimum Rank with Hysteresis Objective Function


Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification

The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.


A survey of hard real-time scheduling for multiprocessor systems

This survey covers hard real-time scheduling algorithms and schedulability analysis techniques for homogeneous multiprocessor systems. It reviews the key results in this field from its origins in the late 1960s to the latest research published in late 2009. The survey outlines fundamental results about multiprocessor real-time scheduling that hold independent of the scheduling algorithms employed. It provides a taxonomy of the different scheduling methods, and considers the various performance metrics that can be used for comparison purposes. A detailed review is provided covering partitioned, global, and hybrid scheduling algorithms, approaches to resource sharing, and the latest results from empirical investigations. The survey identifies open issues, key research challenges, and likely productive research directions.


Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection.

Context Anxiety and depression are both common in primary care patients, but much less attention has been paid to anxiety. Contribution The authors administered a 7-item anxiety scale (Generalized Anxiety Disorder [GAD]-7) to 965 primary care patients, who also had a structured interview, to detect an anxiety disorder. Of these patients, 19.5% had at least 1 anxiety disorder. Patients with anxiety had worse functional status, more disability days, and more physician visits, but 41% were not being treated for any anxiety disorder. The GAD-7 had high sensitivity and good specificity for detecting a generalized anxiety disorder, panic disorder, social anxiety disorder, and posttraumatic stress disorder. Implications Anxiety disorders are common, underrecognized, and undertreated, but they are easy to detect with a brief questionnaire. The Editors Anxiety and depression are the 2 most common mental health problems seen in the general medical setting (15). Although increasing attention has been paid to anxiety, it still lags far behind depression in terms of research as well as clinical and public health efforts in screening, diagnosis, and treating affected individuals. This is unfortunate given the prevalence of anxiety and its substantial impact on patient functioning, work productivity, and health care costs (614). More than 30 million Americans have a lifetime history of anxiety (15), and anxiety disorders cost an estimated $42 billion dollars per year in the United States alone, counting direct and indirect costs (16). The 4 most common anxiety disorders (excluding simple phobias that seldom present clinically) are generalized anxiety disorder, panic disorder, social anxiety disorder, and posttraumatic stress disorder (PTSD) (1723). However, despite the substantial disability associated with each anxiety disorder and the availability of effective treatments, only a minority of patients (15% to 36%) with anxiety are recognized in primary care (24, 25). In our paper, we analyze results from a large primary carebased anxiety study (26) to answer several questions. First, what is the prevalence of these 4 anxiety disorders, both individually and concurrent with one another? Second, how do these disorders compare in functional impairment, health care use, and comorbid depressive and somatic symptom burden? Third, how effective is a brief anxiety measure in screening for each disorder? Compared with previous research, our study is particularly well-positioned to ascertain commonalities among anxiety diagnoses that are traditionally considered to be discrete and to determine whether a single measure can be used as a first step, common metric. This is especially salient for the busy, complex primary care setting, in which simplifying initial recognition of mental disorders may in fact make wider efforts at recognition more feasible. Methods Patient Sample The Patient Health Questionnaire (PHQ) anxiety study (26) was conducted to develop a short measure to assess generalized anxiety disorder. Patients were enrolled from a research network of 15 primary care sites (13 family practice and 2 internal medicine sites) located in 12 states and administered centrally by Clinvest, Inc., Springfield, Missouri, from November 2004 to June 2005. The Generalized Anxiety Disorder (GAD)-7 scale was developed and validated in 2149 patients. In the original study, 2982 persons were invited to participate; of these, 2740 (92%) completed the 4-page questionnaire and had no or minimal missing data (26). To minimize sampling bias, consecutive patients were approached at each site in clinic sessions until the target quota for that week was achieved. Of the 2740 participants, the first 2149 were used for development and validation of the GAD-7 scale, whereas the last 591 were used to determine the testretest reliability of the scale. Of the 2149 patients in the validation group, 1654 agreed to a telephone interview, of whom 965 were randomly selected to undergo this interview within 1 week of their clinic visit by 1 of 2 mental health professionals: a clinical psychologist (with a PhD) or a senior psychiatric social worker. Contact information was sent by fax to each interviewer, who shuffled the fax sheets received each day and then drew from the stack several participants to interview that day. The 965 interviewed patients comprise the study population for this paper, and compared with the 1184 participants who did not undergo a mental health professional interview, these were more often women (69% vs. 63%; P= 0.003) and had slightly higher GAD-7 anxiety scores (5.7 vs. 5.1; P= 0.010) but were similar in age, race, and education. Of note, we only used data from the 1184 participants not undergoing a mental health professional interview to derive the GAD-7 (26). The study was approved by the Sterling Institutional Review Board. Study Questionnaire Before seeing their physicians, patients completed a 4-page questionnaire that included the GAD-7 (Appendix Figure). This scale was shown to have good internal and testretest reliability, as well as convergent, construct, criterion, procedural, and factorial validity for the diagnosis of generalized anxiety disorder (26). Scores on the GAD-7 range from 0 to 21; scores of 5, 10, and 15 represent mild, moderate, and severe anxiety symptoms, respectively. The first 2 items of the GAD-7 represent core anxiety symptoms, and scores on this GAD-2 subscale range from 0 to 6. Appendix Figure. The Generalized Anxiety Disorder (GAD)-7 scale. The first 2 items constitute the GAD-2 subscale. GAD-7 2006 Pfizer Inc. All rights reserved. Used with permission. The study questionnaire also included questions about age, sex, education, race or ethnicity, and marital status; the Medical Outcomes Study Short Form-20 (SF-20), which measures functional status in 6 domains (27); the 10-item anxiety subscale from the Hopkins Symptom Checklist (28); the PHQ-8 depression scale (29); a 3-item version of the Social Phobia Inventory (Mini-SPIN) (30); the 5-item PHQ panic module (25); and the PHQ-15 somatic symptom scale (31). Also, single-item global assessments of anxiety, depression, and pain based on a scale of 0 (none) to 10 (as bad as you can imagine) were included. Finally, patients reported the number of physician visits and disability days during the previous 3 months. Structured Psychiatric Interview The 2 mental health professionals, while blinded to the results of the self-report research questionnaire, conducted structured psychiatric interviews by telephone to establish independent criteria-based diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (32). The interview consisted of the generalized anxiety disorder, social anxiety disorder, and PTSD sections of the Structured Clinical Interview for DSM-IV (SCID) (33). Reinterview by telephone was used because of its feasibility in our multisite study and its demonstrated comparability with face-to-face research interviews (3436). The 2 mental health professionals based diagnoses of generalized anxiety disorder and PTSD on the SCID interview. For generalized anxiety disorder, some questions were slightly modified to better assess each DSM-IV criterion. They based a diagnosis of social anxiety disorder on whether the patient met SCID diagnostic criteria and had a Mini-SPIN score of 8 or greater, because this improves the accuracy of social anxiety disorder diagnoses (37). They based a diagnosis of panic disorder on answering yes to all 5 questions on the PHQ panic module, a threshold that reflects DSM-IV criteria and has been validated in both clinical (25) and population-based (38) samples. Statistical Analysis We estimated sample size with respect to sensitivity of the GAD-7 scale for diagnosing the target disease (generalized anxiety disorder). We needed 60 participants with generalized anxiety disorder to ensure that the total width of the 95% CI around a sensitivity proportion of 0.80 was no greater than 0.20. Given that the estimated prevalence of generalized anxiety disorder in the primary care population was 6% (18), we needed a total of 1000 unselected primary care patients to have approximately 60 patients with generalized anxiety disorder. We determined the prevalence of each of the 4 anxiety disorders and compared them in patient demographic characteristics, functional status, psychiatric comorbidity, disability days, and physician visits. Consistent with previous work (1, 26, 29, 39), we replaced missing values in a scale with the mean value of the remaining items if 25% or fewer items were missing. If more than 25% of items were missing, the sum score was not computed and was counted as missing. The amount of missing data for any individual variable or scale score was very low (<1%). The 15 sites did not differ in missing data. In addition to descriptive statistics, we used analysis of covariance to examine associations among each anxiety disorder and the 6 SF-20 functional status scales, self-reported disability days, and physician visitscontrolling for demographic variables (sex, age, race, and educational level) and study site. We ran similar models to examine the effect of the number of anxiety disorders. In all models, patients with no anxiety disorder were the reference group. We adjusted pairwise statistical comparisons by using the Bonferroni correction. Because some dependent variables displayed a skewed (but unimodal) distribution, we also reran the models using the rank transformation of the dependent variables. We examined the operating characteristics (sensitivity, specificity, and positive likelihood ratio) for a range of cutoff scores of the GAD-7 and GAD-2 for each anxiety disorder. We conducted receiver-operating characteristic curve analyses to determine the area under the curve (AUC) for each anxiety disorder. We calculated AUCs and performed statistical comparisons (GAD-7 vs. GAD-


Parental Socio-Economic Status and Students ’ Academic Achievement in Selected Secondary Schools in Urban Informal Settlements in Westlands Division , Nairobi County .

This study sought to investigate critical parental socio-economic factors affecting the academic achievement of students in selected secondary schools in urban informal settlements in Westlands District in Nairobi County. The study was premised on the classical Liberal Theory of Equal Opportunity and social Darwinism proposed by Charles Darwin. A descriptive survey design using a sample of 125 respondents comprising of 91 students, 18 teachers and 16 parents was used for the study. The tools for data collection were questionnaires for students, Focus Group Discussions for teachers and Interview schedules for parents. Quantitative data from the questionnaires was analyzed using descriptive and inferential statistics while the qualitative data from interviews was managed through thematic techniques. The major findings of the study were that; the physical and other critical instructional resources were grossly inadequate or in a pathetic condition not conducive to education provision, there was a strong non-significant negative correlation between the occupation of parents and ability to finance education, that there is a significant positive correlation between good parent-teacher relationship and their involvement in their children’s academic achievement. It was concluded that, parental occupation and involvement in learning activities and effective parent-teacher relationship were facilitating factors. Parents’ low ability to finance education, coupled with the poor status of physical and instructional resources were inhibiting factors to students’ academic achievement in the study locale. It was therefore recommended that the government should strengthen the collaboration between key education development partners to mobilize physical teaching/learning resources and strengthen education in the region. Unemployment should be controlled; poor students should be provided scholarships and that the government should take steps to raise socioeconomic status of people.


Bidirectional Dynamics for Protein Secondary Structure Prediction

Connectionist models for learning in sequential domains are typically dynamical systems that use hidden states to store contextual information. In principle, these models can adapt to variable time lags and perform complex sequential mappings. In spite of several successful applications (mostly based on hidden Markov models), the general class of sequence learning problems is still far from being satisfactorily solved. In particular, learning sequential translations is generally a hard task and current models seem to exhibit a number of limitations. One of these limitations, at least for some application domains, is the causality assumption. A dynamical system is said to be causal if the output at (discrete) time t does not depend on future inputs. Causality is easy to justify in dynamics that attempt to model the behavior of many physical systems. Clearly, in these cases the response at time t cannot depend on stimulae that the system has not yet received as input. As it turns out, non-causal dynamics over infinite time horizons cannot be realized by any physical or computational device. For certain categories of finite sequences, however, information from both the past and the future can be very useful for analysis and predictions at time t. This is the case, for example, of DNA and protein sequences where the structure and function of a region in the sequence may strongly depend on events located both upstream and downstream of the region, sometimes at considerable distances. Another good example is provided by the off-line translation of a language into another one. Even in the so-called “simultaneous” translation, it is well known that interpreters are constantly forced to introduce small delays in order to acquire “future” information within a sentence to resolve semantic ambiguities and preserve syntactic correctness. Non-causal dynamics are sometimes used in other disciplines (for example, Kalman smoothing in optimal control or non-causal digital filters in signal processing). However, as far as connectionist models are concerned, the causality assumption is shared among all the types of models which are capable of mapping input sequences to output sequences, including recurrent neural networks and input-output HMMs (IOHMMs) (Bengio & Frasconi, 1996). In this paper, we develop a new family of non-causal adaptive architectures where the underlying dynamics are factored using a pair of chained hidden state variables. The two


Analog inference circuits for deep learning

Deep Machine Learning (DML) algorithms have proven to be highly successful at challenging, high-dimensional learning problems, but their widespread deployment is limited by their heavy computational requirements and the associated power consumption. Analog computational circuits offer the potential for large improvements in power efficiency, but noise, mismatch, and other effects cause deviations from ideal computations. In this paper we describe circuits useful for DML algorithms, including a tunable-width bump circuit and a configurable distance calculator. We also discuss the impacts of computational errors on learning performance. Finally we will describe a complete deep learning engine implemented using current-mode analog circuits and compare its performance to digital equivalents.


Multiresolution EO/IR target tracking and identification

Simultaneous target tracking and identification through feature association, attribute matching, or blob analysis is dependent on spatio-temporal measurements. Improved track maintenance should be achievable by maintaining coarse sensor resolutions on maneuvering targets and utilizing finer sensor resolutions to resolve closely-spaced targets. There are inherent optimal resolutions for sensors and restricted altitudes that constrain operational performance that a sensor manager must optimize for both wide-area surveillance and precision tracking. The advent of better optics, coordinated sensor management, and fusion strategies provide an opportunity to enhance simultaneous tracking and identification algorithms. We investigate utilizing electro-optical (EO) and infrared (IR) sensors operating at various resolutions to optimize target tracking and identification. We use a target-dense maneuvering scenario to highlight the performance gains with the multiresolution EO/IR data association (MEIDA) algorithm in tracking crossing targets.


Benchmarking Single-Image Dehazing and Beyond

We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics to no-reference metrics and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.


Effects of Platform and Content Attributes on Information Dissemination on We Media: A Case Study on WeChat Platform

The emergence of the WeChat has brought some new changes to people's ways of life and reading. Thanks to the WeChat Public Platform, many people from all walks of life have become We Media operators, creating their own brands and products. In a case study of WeChat Public Platform, this paper uses NLPIR, a tool for Chinese word segmentation, and SPSS, a data analysis software, to conduct an analysis of actual data captured by Web Spider about WeChat posts and the platform itself, exploring the effects of post content and platform attributes on users' reading and dissemination behaviors. Our regression analysis found that some content attributes, including parts of speech like adverb, punctuation marks such as question mark, and headline length and position, as well as platform attributes, such as the platform seniority, have significant impacts on page views; the genres of advertisement, joke and warning, among other content attributes, and follower count as a platform attribute, have a very significant effect on the dissemination index. This paper is helpful for operators to gain a better understanding of factors influencing the spread of information published by WeChat Official Accounts and providing valuable revelations on achieving fast and efficient dissemination of posts.


Mining Local Process Models

In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as local process models. Local process model mining can be positioned in-between process discovery and episode / sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode / sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.


Marketing Theory : Overview of Ontology , Epistemology , and Axiology Aspects

This article discusses about the marketing theory from the perspective of philosophy of science. The discussion focused on the aspects of the ontology, epistemology, and axiology. From the aspect of the ontology, it seems that the essence of marketing is a useful science and mutually beneficial for both the marketer and the stakeholders, or in other words that marketing knowledge is useful knowledge for the benefit of humankind that can be realized through the exchange process. Side of the ontology covers what the substance of marketing knowledge, the substance of truth and reality that is inherent with marketing. Meanwhile, aspects of epistemology cover a variety of approaches, methods, sources, structure and validation or marketing truth. Finally, axiology fields are related to ethics in marketing and marketing research. Marketing ethics and ethics in marketing research is a crucial matter, including in this case is trust.


Adversarial Objectives for Text Generation

Language models can be used to generate text by iteratively sampling words conditioned on previously sampled words. In this work, we explore adversarial objectives to obtain good text generations by training a recurrent language model to keep its hidden state statistics during sampling similar to what it has sees during maximum likelihood estimation (MLE) training. We analyze the convergence of these models and discuss the effect of the adversarial objective on word-level and character-level language models. We find that using an adversarial objective assists the MLE objective and results in faster convergence and lower validation perplexities for character-level language models. We also collect sentence quality ratings from human participants and show that character-level language models with the adversarial objective generates qualitatively better sentences than the standard character-level recurrent language model.


A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.


Metadating: Exploring the Romance and Future of Personal Data

We introduce Metadating -- a future-focused research and speed-dating event where single participants were invited to "explore the romance of personal data". Participants created "data profiles" about themselves, and used these to "date" other participants. In the rich context of dating, we study how personal data is used conversationally to communicate and illustrate identity. We note the manner in which participants carefully curated their profiles, expressing ambiguity before detail, illustration before accuracy. Our findings proposition a set of data services and features, each concerned with representing and curating data in new ways, beyond a focus on purely rational or analytic relationships with a quantified self. Through this, we build on emerging interest in "lived informatics" and raise questions about the experience and social reality of a "data-driven life".


Noise and sensitivity analysis of harmonic radar system for vital sign detection

Harmonic radar architectures present systems of wireless detection operating at multiple harmonic carrier frequencies. The noise and sensitivity of a harmonic radar system, which is developed at 12 GHz and 24 GHz for vital signs detection, are studied numerically and experimentally. The received signal power of the radar system is analyzed numerically as a function of the distance, taking into account the radar cross section (RCS) of the patient chest. The numerical data are compared with results obtained with single carrier frequency system. The total noise is a combination of thermal noise, residual phase noise, and flicker noise that presents the most elevated factor influencing the detection at baseband. Experimental results show that with the harmonic radar, the flicker noise can be reduced by 20dB at around 1Hz baseband frequency.


DNA Sequence Compression Using the Burrows-Wheeler Transform

We investigate off-line dictionary oriented approaches to DNA sequence compression, based on the Burrows-Wheeler Transform (BWT). The preponderance of short repeating patterns is an important phenomenon in biological sequences. Here, we propose off-line methods to compress DNA sequences that exploit the different repetition structures inherent in such sequences. Repetition analysis is performed based on the relationship between the BWT and important pattern matching data structures, such as the suffix tree and suffix array. We discuss how the proposed approach can be incorporated in the BWT compression pipeline.


Multichannel-Sniffing-System for Real-World Analysing of Wi-Fi-Packets

Wireless technologies like Wi-Fi send their data using multiple channels. To analyze an environment and all Wi-Fi packets inside, a sniffing system is needed, which can sniff on all used channels of the wireless technology at the same time. This allows catching most packets on each channel. In this paper, a way to build up a multi-channel-sniffing-system (MCSS) is described. The test system uses several single board computers (SBC) with an external Wi-Fi adapter (USB), 19 SBCs are sniffing nodes (SFN) and one SBC as sending node (SN). The sniffing SBCs are placed in a cycle around the sender so that every node has the same chance to receive the simulated packets from the SN. For the control of all 20 SBCs, a self-developed software is used, which connects from the host to the clients and is used for configuring the experiments. The configuration is sent to each client and will initiate their start, so that their times are also synchronized, for this all clients are synchronised using a time


Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition

Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented


G2RHA:Group-to-Route Handover Authentication Scheme for Mobile Relays in LTE-A High-Speed Rail Networks

The introduction of mobile relay node (MRN) in long-term evolution advanced high-speed rail networks is an attractive method to provide uninterrupted connectivity for the group of user equipments on board. However, MRNs still suffer from frequent handovers and several security threats due to several rounds of message exchange and the insecure air interface between MRNs and donor eNBs (DeNBs). In this paper, we propose a group-to-route handover authentication scheme based on trajectory prediction for mobile relays. By our scheme, all of the DeNBs deployed along the trajectory can be formed a route-DeNB group and all of the MRNs deployed in the same train can construct a MRN group. Compared with the current 3GPP standards and other related schemes, our scheme can accomplish the mutual authentication and key agreement between the MRN group and the target DeNB with ideal efficiency in terms of authentication signaling overhead, bandwidth consumption, and computational cost. Security analysis by using the BAN logic and formal verification tool Scyther show that our scheme can work correctly and withstand several protocol attacks.


Using institutional theory and dynamic simulation to understand complex e-Government phenomena

Available online 17 May 2011


Color retinal image enhancement using CLAHE

Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.


An Analog Integrated Circuit Beamformer for High-Frequency Medical Ultrasound Imaging

We designed and fabricated a dynamic receive beamformer integrated circuit (IC) in 0.35-μm CMOS technology. This beamformer IC is suitable for integration with an annular array transducer for high-frequency (30-50 MHz) intravascular ultrasound (IVUS) imaging. The beamformer IC consists of receive preamplifiers, an analog dynamic delay-and-sum beamformer, and buffers for 8 receive channels. To form an analog dynamic delay line we designed an analog delay cell based on the current-mode first-order all-pass filter topology, as the basic building block. To increase the bandwidth of the delay cell, we explored an enhancement technique on the current mirrors. This technique improved the overall bandwidth of the delay line by a factor of 6. Each delay cell consumes 2.1-mW of power and is capable of generating a tunable time delay between 1.75 ns to 2.5 ns. We successfully integrated the fabricated beamformer IC with an 8-element annular array. Experimental test results demonstrated the desired buffering, preamplification and delaying capabilities of the beamformer.


Multi-target threat assessment for automotive applications

Auto-brake systems have been on the market for a few years, but in order to continue improving their performance, multi-target threat assessment is a key component. The goal is to early be able to say that a collision can no longer be avoided by a steering maneuver, and thus braking can be started earlier. This is done by looking at all objects in the road scene, including other vehicles and barriers, and then looking for escape paths. The paper presents a computationally efficient way of determining whether an escape path can be found in a scenario with generally positioned objects, given the limited dynamics of the vehicle.


Managing Organizational Knowledge By Diagnosing Intellectual Capital : Framing and Advancing the State of the Field

Copyright © 2001, Idea Group Publishing. Since organizational knowledge is at the crux of sustainable competitive advantage, the burgeoning field of intellectual capital is an exciting area for both researchers and practitioners. Intellectual capital is conceptualized from numerous disciplines making the field a mosaic of perspectives. Accountants are interested in how to measure it on the balance sheet, information technologists want to codify it on systems, sociologists want to balance power with it, psychologists want to develop minds because of it, human resource managers want to calculate an ROI on it, and training and development officers want to make sure that they can build it. The following article represents a comprehensive literature review from a variety of managerial disciplines. In addition to highlighting the research to date, avenues for future pursuit are also offered.


Intellectual capital ROI : a causal map of human capital antecedents and consequents

This report describes the results of a ground-breaking research study that measured the antecedents and consequents of effective human capital management. The research sample consisted of 76 senior executives from 25 companies in the financial services industry. The results of the study yielded a holistic causal map that integrated constructs from the fields of intellectual capital, knowledge management, human resources, organizational behaviour, information technology and accounting. The integration of both quantitative and qualitative measures in an overall conceptual model yielded several research implications. The resulting structural equation model allows participating organizations and researchers to gauge the effectiveness of an organization’s human capital capabilities. This will allow practitioners and researchers to more efficiently allocate resources with regard to human capital management. The potential outcomes of the study are limitless, since a program of consistent re-evaluation can lead to the establishment of causal relationships between human capital management and economic and business results. Introduction Today’s knowledge-based world consists of universal dynamic change and massive information bombardment. By the year 2010, the codified information base of the world is expected to `̀ double every 11 hours’’ (Bontis, 1999, p. 435). Information storage capacities continue to expand enormously. In 1950, IBM’s Rama C tape contained 4.4 megabytes and they were able to store as many as 50 of these tapes together. At that time, 220 megabytes represented the frontiers of information storage. Many of today’s standard desktop computers are being sold with 40 gigabytes of hard disk space. It is sobering to remember that full motion video in uncompressed form requires 1 gigabyte per minute and that the 83 minutes of Snow White digitized in full colour amount to 15 terabytes of space. Unfortunately, the conscious mind is only capable of processing somewhere between 16 and 40 bits of information (ones and zeros) per second. How do we reconcile this information bombardment conundrum when it seems that human beings are the bottle-neck? The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/1469-1930.htm The authors would like to acknowledge the following organizations for their financial support: Accenture, Saratoga Institute and the Institute for Intellectual Capital Research. The authors would also like to highlight the contribution of Vanessa Yeh, who administered the data collection phase of this research.


Producing sustainable competitive advantage through the effective management of people *

Executive Overview Achieving competitive success through people involves fundamentally altering how we think about the workforce and the employment relationship. It means achieving success by working with people, not by replacing them or limiting the scope of their activities. It entails seeing the workforce as a source of strategic advantage, not just as a cost to be minimized or avoided. Firms that take this different perspective are often able to successfully outmaneuver and outperform their rivals. ........................................................................................................................................................................


HOW MUCH DOES INDUSTRY MATTER , REALLY ?

In this paper, we examine the importance of year, industry, corporate-parent, and businessspecific effects on the profitability of U.S. public corporations within specific 4-digit SIC categories. Our results indicate that year, industry, corporate-parent, and business-specific effects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the aggregate variance in profitability. We also find that the importance of the effects differs substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation. Across all sectors we find a negative covariance between corporate-parent and industry effects. A detailed analysis suggests that industry, corporate-parent, and business-specific effects are related in complex ways.  1997 by John Wiley & Sons, Ltd.


From sunlight to phytomass: on the potential efficiency of converting solar radiation to phyto-energy.

The relationship between solar radiation capture and potential plant growth is of theoretical and practical importance. The key processes constraining the transduction of solar radiation into phyto-energy (i.e. free energy in phytomass) were reviewed to estimate potential solar-energy-use efficiency. Specifically, the out-put:input stoichiometries of photosynthesis and photorespiration in C(3) and C(4) systems, mobilization and translocation of photosynthate, and biosynthesis of major plant biochemical constituents were evaluated. The maintenance requirement, an area of important uncertainty, was also considered. For a hypothetical C(3) grain crop with a full canopy at 30°C and 350 ppm atmospheric [CO(2) ], theoretically potential efficiencies (based on extant plant metabolic reactions and pathways) were estimated at c. 0.041 J J(-1) incident total solar radiation, and c. 0.092 J J(-1) absorbed photosynthetically active radiation (PAR). At 20°C, the calculated potential efficiencies increased to 0.053 and 0.118 J J(-1) (incident total radiation and absorbed PAR, respectively). Estimates for a hypothetical C(4) cereal were c. 0.051 and c. 0.114 J J(-1), respectively. These values, which cannot be considered as precise, are less than some previous estimates, and the reasons for the differences are considered. Field-based data indicate that exceptional crops may attain a significant fraction of potential efficiency.


App Miscategorization Detection: A Case Study on Google Play

An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+. Experiments on Google Play shows that FRAC+’s topics are more aligned with Google’s new categories and 0.35-1.10 percent game apps are detected to be miscategorized.


Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image Tone Mapping

A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.


Using neural networks and GIS to forecast land use changes : a Land Transformation Model

The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model’s predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan’s Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales. # 2002 Elsevier Science Ltd. All rights reserved.


Business & IT Alignment in Theory and Practice

A key success factor for a successful company in a dynamic environment is effective and efficient information technology (IT) supporting business strategies and processes. In recent surveys however it is concluded that in most companies IT is not aligned with business strategy. The alignment between business needs and IT capabilities is therefore still a prominent area of concern. This paper reports the first stages of a research program exploring the differences of business & IT alignment (BIA) in theory and in practice. The paper presents an overview of the development of theory on BIA and reports the issues with aligning IT to business in practice based on a number of focus-group discussions with CIOs and IT managers. In line with the practical approach to BIA that the CIOs and IT managers in the focus-groups took, the last part of the paper builds upon Luftman's BIA maturity model and reports the application of the model to 12 Dutch firms


Blockstack : A New Decentralized Internet

The traditional internet has many central points of failure and trust, like (a) the Domain Name System (DNS) servers, (b) public-key infrastructure, and (c) end-user data stored on centralized data stores. We present the design and implementation of a new internet, called Blockstack, where users don’t need to trust remote servers. We remove any trust points from the middle of the network and use blockchains to secure critical data bindings. Blockstack implements services for identity, discovery, and storage and can survive failures of underlying blockchains. The design of Blockstack is informed by three years of experience from a large blockchain-based production system. Blockstack gives comparable performance to traditional internet services and enables a much-needed security and reliability upgrade to the traditional internet.


The impact of modified Hatha yoga on chronic low back pain: a pilot study.

PURPOSE
The purpose of this randomized pilot study was to evaluate a possible design for a 6-week modified hatha yoga protocol to study the effects on participants with chronic low back pain.

PARTICIPANTS
Twenty-two participants (M = 4; F = 17), between the ages of 30 and 65, with chronic low back pain (CLBP) were randomized to either an immediate yoga based intervention, or to a control group with no treatment during the observation period but received later yoga training.

METHODS
A specific CLBP yoga protocol designed and modified for this population by a certified yoga instructor was administered for one hour, twice a week for 6 weeks. Primary functional outcome measures included the forward reach (FR) and sit and reach (SR) tests. All participants completed Oswestry Disability Index (ODI) and Beck Depression Inventory (BDI) questionnaires. Guiding questions were used for qualitative data analysis to ascertain how yoga participants perceived the instructor, group dynamics, and the impact of yoga on their life.

ANALYSIS
To account for drop outs, the data were divided into better or not categories, and analyzed using chi-square to examine differences between the groups. Qualitative data were analyzed through frequency of positive responses.

RESULTS
Potentially important trends in the functional measurement scores showed improved balance and flexibility and decreased disability and depression for the yoga group but this pilot was not powered to reach statistical significance. Significant limitations included a high dropout rate in the control group and large baseline differences in the secondary measures. In addition, analysis of the qualitative data revealed the following frequency of responses (1) group intervention motivated the participants and (2) yoga fostered relaxation and new awareness/learning.

CONCLUSION
A modified yoga-based intervention may benefit individuals with CLB, but a larger study is necessary to provide definitive evidence. Also, the impact on depression and disability could be considered as important outcomes for further study. Additional functional outcome measures should be explored. This pilot study supports the need for more research investigating the effect of yoga for this population.


An Overview on Evaluating and Predicting Scholarly Article Impact

Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.


"An Odd Kind of Pleasure": Differentiating Emotional Challenge in Digital Games

Recent work introduced the notion of emotional challenge as a means to afford more unique and diverse gaming experiences. However, players' experience of emotional challenge has received little empirical attention. It remains unclear whether players enjoy it and what exactly constitutes the challenge thereof. We surveyed 171 players about a challenging or an emotionally challenging experience, and analyzed their responses with regards to what made the experience challenging, their emotional response, and the relation to core player experience constructs. We found that emotional challenge manifested itself in different ways, by confronting players with difficult themes or decisions, as well as having them deal with intense emotions. In contrast to more'conventional' challenge, emotional challenge evoked a wider range of negative emotions and was appreciated significantly more by players. Our findings showcase the appeal of uncomfortable gaming experiences, and extend current conceptualizations of challenge in games.


RFID Systems: A Survey on Security Threats and Proposed Solutions

Low-cost Radio Frequency Identification (RFID) tags affixed to consumer items as smart labels are emerging as one of the most pervasive computing technology in history. This can have huge security implications. The present article surveys the most important technical security challenges of RFID systems. We first provide a brief summary of the most relevant standards related to this technology. Next, we present an overview about the state of the art on RFID security, addressing both the functional aspects and the security risks and threats associated to its use. Finally, we analyze the main security solutions proposed until date.


Broadband Substrate Integrated Waveguide Cavity-Backed Bow-Tie Slot Antenna

A novel design technique for broadband substrate integrated waveguide cavity-backed slot antenna is demonstrated in this letter. Instead of using a conventional narrow rectangular slot, a bow-tie-shaped slot is implemented to get broader bandwidth performance. The modification of the slot shape helps to induce strong loading effect in the cavity and generates two closely spaced hybrid modes that help to get a broadband response. The slot antenna incorporates thin cavity backing (height <;0.03λ0 ) in a single substrate and thus retains low-profile planar configuration while showing unidirectional radiation characteristics with moderate gain. A fabricated prototype is also presented that shows a bandwidth of 1.03 GHz (9.4%), a gain of 3.7 dBi over the bandwidth, 15 dB front-to-back ratio, and cross-polarization level below -18 dB.


Sparse PCA via Bipartite Matchings

We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but as we show this greedy procedure is suboptimal. We present a novel algorithm for sparse PCA that jointly optimizes multiple disjoint components. The extracted features capture variance that lies within a multiplicative factor arbitrarily close to 1 from the optimal. Our algorithm is combinatorial and computes the desired components by solving multiple instances of the bipartite maximum weight matching problem. Its complexity grows as a low order polynomial in the ambient dimension of the input data matrix, but exponentially in its rank. However, it can be effectively applied on a low-dimensional sketch of the data; this allows us to obtain polynomial-time approximation guarantees via spectral bounds. We evaluate our algorithm on real data-sets and empirically demonstrate that in many cases it outperforms existing, deflationbased approaches.


Low-power high-speed level shifter design for block-level dynamic voltage scaling environment

Two novel level shifters that are suitable for block-level dynamic voltage scaling environment (namely, V/sub DD/-hopping) are proposed. In order to achieve reduction in power consumption and delay, the first proposed level shifter which is called contention mitigated level shifter (CMLS) uses a contention-reduction technique. The simulation results with 65-nm CMOS model show 24% reduction in power and 50% decrease in delay with 4% area increase compared with the conventional level shifter. The second proposed level shifter which is called bypassing enabled level shifter (BELS) implements a bypass function and it is fabricated using 0.35/spl mu/m CMOS technology. The measurement results show that the power and delay of the proposed BELS are reduced by 50% and 65%, respectively with 60% area overhead over the conventional level shifter.


An accurate algebraic solution for moving source location using TDOA and FDOA measurements

This paper proposes an algebraic solution for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of receivers. The method employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. It does not have the initialization and local convergence problem as in the conventional linear iterative method. The estimated accuracy of the source position and velocity is shown to achieve the Crame/spl acute/r-Rao lower bound for Gaussian TDOA and FDOA noise at moderate noise level before the thresholding effect occurs. Simulations are included to examine the algorithm's performance and compare it with the Taylor-series iterative method.


Towards dropout training for convolutional neural networks

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.


Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview

Bridge is an international imperfect information game played with similar rules all over the world and it is played by millions of players. It is an intelligent game; it increases creativity and knowledge of human mind. Many of the researchers analyses the Bridge bidding and playing phases, and they developed many programs for getting better results. The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding


High-Gain Reconfigurable Sectoral Antenna Using an Active Cylindrical FSS Structure

A novel design of a high-gain reconfigurable sectoral antenna using an active cylindrical frequency selective surface (FSS) structure is presented. The FSS structure consists of metallic discontinuous strips with PIN diodes in their discontinuities, and it is placed cylindrically around an omnidirectional electromagnetically coupled coaxial dipole (ECCD) array. The cylindrical FSS structure is divided into two semi-cylinders. By controlling the state of diodes in each semi-cylinder, a directive radiation pattern is obtained that can be swept in the entire azimuth plane. The effect of the diode-state configuration and the radius of the cylindrical structure are carefully studied to obtain an optimum sectoral radiation pattern. In addition, a solution for increasing the matching bandwidth of the antenna is also proposed. An experimental prototype was fabricated, and the measured results show a beamwidth of 20° in elevation and 70° in the azimuth plane at 2.1 GHz with a gain of 13 dBi. With these features, the proposed antenna is suitable for base-station applications in wireless communication systems.


Methodology for customer relationship management

Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers. This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM. This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement. 2005 Elsevier Inc. All rights reserved.