query: Feature Extraction and Duplicate Detection for Text Mining : A Survey

positive: 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.

positive: 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.

positive: 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

positive: Fast algorithms for mining association rules

positive: 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

negative: The Minimum Rank with Hysteresis Objective Function

negative: 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.

negative: 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.

negative: 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-

negative: 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.

negative: 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

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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".

negative: 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.

negative: 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.

negative: 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

negative: 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

negative: Manga content analysis using physiological signals Recently, the physiological signals have been analyzed more and more, especially in the context of everyday life activities such as watching video or looking at pictures. Tracking these signals gives access to the mental state of the user (interest, tiredness, stress) but also to his emotions (sadness, fright, happiness). The analysis of the reader's physiological signals during reading can provide a better understanding of the reader's feelings but also a better understanding of the documents. Our main research direction is to find the relationship between a change in the reader's physiological signal and the content of the reading. As a first step, we investigate whether it is possible to distinguish a manga (Japanese comic book) from another by analyzing the physiological signals of the reader. We use 3 different manga genres (horror, romance, comedy) and try to predict which one is read by analyzing the features extracted from the physiological signals of the reader. Our method uses the blood volume pulse, the electrodermal activity and the skin temperature of the reader while reading. We show that by using these physiological signals with a support vector machine we can retrieve which manga has been read with a 90% average accuracy.

negative: 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.

negative: Using institutional theory and dynamic simulation to understand complex e-Government phenomena Available online 17 May 2011

negative: 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.

negative: 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.

negative: 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.

negative: DuDe: The Duplicate Detection Toolkit Duplicate detection, also known as entity matching or record linkage, was first defined by Newcombe et al. [19] and has been a research topic for several decades. The challenge is to effectively and efficiently identify pairs of records that represent the same real world entity. Researchers have developed and described a variety of methods to measure the similarity of records and/or to reduce the number of required comparisons. Comparing these methods to each other is essential to assess their quality and efficiency. However, it is still difficult to compare results, as there usually are differences in the evaluated datasets, the similarity measures, the implementation of the algorithms, or simply the hardware on which the code is executed. To face this challenge, we are developing the comprehensive duplicate detection toolkit “DuDe”. DuDe already provides multiple methods and datasets for duplicate detection and consists of several components with clear interfaces that can be easily served with individual code. In this paper, we present the DuDe architecture and its workflow for duplicate detection. We show that DuDe allows to easily compare different algorithms and similarity measures, which is an important step towards a duplicate detection benchmark. 1. DUPLICATE DETECTION FRAMEWORKS The basic problem of duplicate detection has been studied under various names, such as entity matching, record linkage, merge/purge or record reconciliation. Given a set of entities, the goal is to identify the represented set of distinct real-world entities. Proposed algorithms in the area of duplicate detection aim to improve the efficiency or the effectiveness of the duplicate detection process. The goal of efficiency is usually to reduce the number of pairwise comparisons. In a naive approach this is quadratic in the number of records. By making intelligent guesses which records have a high probability of representing the same real-world entity, the search space is reduced with the drawback that some duPermission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, to post on servers or to redistribute to lists, requires a fee and/or special permission from the publisher, ACM. VLDB ‘10, September 13-17, 2010, Singapore Copyright 2010 VLDB Endowment, ACM 000-0-00000-000-0/00/00. plicates might be missed. Effectiveness, on the other hand, aims at classifying pairs of records accurately as duplicate or non-duplicate [17]. Elmagarmid et al. have compiled a survey of existing algorithms and techniques for duplicate detection [11]. Köpcke and Rahm give a comprehensive overview about existing duplicate detection frameworks [15]. They compare eleven frameworks and distinguish between frameworks without training (BN [16], MOMA [24], SERF [1]), training-based frameworks (Active Atlas [22], [23], MARLIN [2, 3], Multiple Classifier System [27], Operator Trees [4]) and hybrid frameworks (TAILOR [10], FEBRL [6], STEM [14], Context Based Framework [5]). Not included in the overview is STRINGER [12], which deals with approximate string matching in large data sources. Köpcke and Rahm use several comparison criteria, such as supported entity types (e.g. relational entities, XML), availability of partitioning methods to reduce the search space, used matchers to determine whether two entities are similar enough to represent the same real-world entity, the ability to combine several matchers, and, where necessary, the selection of training data. In their summary, Köpcke and Rahm criticize that the frameworks use diverse methodologies, measures, and test problems for evaluation and that it is therefore difficult to assess the efficiency and effectiveness of each single system. They argue that standardized entity matching benchmarks are needed and that researchers should provide prototype implementations and test data with their algorithms. This agrees with Neiling et al. [18], where desired properties of a test framework for object identification solutions are discussed. Moreover, Weis et al. [25] argue for a duplicate detection benchmark. Both papers see the necessity for standardized data from real-world or artificial datasets, which must also contain information about the real-world pairs. Additionally, clearly defined quality criteria with a description of their computation, and a detailed specification of the test procedure are required. An overview of quality and complexity measures for data linkage and deduplication can be found in Christen and Goiser [7] With DuDe, we provide a toolkit for duplicate detection that can easily be extended by new algorithms and components. Conducted experiments are comprehensible and can be compared with former ones. Additionally, several algorithms, similarity measures, and datasets with gold standards are provided, which is a requirement for a duplicate detection benchmark. DuDe and several datasets are available for download at http://www.hpi.uni-potsdam. de/naumann/projekte/dude.html.

negative: Learning deep semantic attributes for user video summarization This paper presents a Semantic Attribute assisted video SUMmarization framework (SASUM). Compared with traditional methods, SASUM has several innovative features. Firstly, we use a natural language processing tool to discover a set of keywords from an image and text corpora to form the semantic attributes of visual contents. Secondly, we train a deep convolution neural network to extract visual features as well as predict the semantic attributes of video segments which enables us to represent video contents with visual and semantic features simultaneously. Thirdly, we construct a temporally constrained video segment affinity matrix and use a partially near duplicate image discovery technique to cluster visually and semantically consistent video frames together. These frame clusters can then be condensed to form an informative and compact summary of the video. We will present experimental results to show the effectiveness of the semantic attributes in assisting the visual features in video summarization and our new technique achieves state-of-the-art performance.

negative: Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method This research work exposes the novel approach of analysis at existing works based on machine vision system for the identification of the visual symptoms of Cotton crop diseases, from RGB images. Diseases regions of cotton crops are revealed in digital pictures, Which were amended and segmented. In this work Proposed Enhanced PSO feature selection method adopts Skew divergence method and user features like Edge,Color,Texture variances to extract the features. Set of features was extracted from each of them. The extracted feature was input to the SVM, Back propagation neural network (BPN),Fuzzy with Edge CYMK color feature and GA feature selection. Tests were performed to identify the best classification model. It has been hypothesized that from the given characteristics of the images, there should be a subset of features more informative of the image domain.To test this hypothesis, three classification models were assessed via crossvalidation. To Evaluate its efficiency of six types of diseases have been accurately classified like Bacterial Blight, Fusariumwilt, Leaf Blight, Root rot, Micro Nurient, Verticilium Wilt.The Experimental results obtained show that the robust feature vector set which is an Enhancement of a feature extraction method (EPSO) has been afforded the performance assessment of this system. Keywords— SVM, BPN, Fuzzy, CMYK and Edge Features, Genetic Algorithm, Cotton leaf data sets., Enhance Particle swarm optimization, Skew divergences features. I. Introduct ion Data mining is the process of extracting patterns from the data and to transform raw data into information.. It is commonly utilized in a broad range of profiling practices, such as marketing, surveillance, fraud detection, agriculture environment and scientific discovery. Among other things, the most prominent features of data mining techniques are clustering and predictive. The benefits of data mining are its capability to gain deeper perceptive of the patterns using current available exposure capabilities . In this work categorization of the cotton leaf diseases are done using data mining with image processing techniques. The main target is to identify the disease in the leaf spot of the cotton crops. In this regard, It is discussed that about 80 to 90 percentage disease on the Cotton crops are on its leaf spot [1]. Consequently areas of interest is that identifying the leaf of the cotton tree rather than whole cotton plant the cotton leaf is mostly suffered from diseases like fungus, virus, Foliar leaf spot of cotton, Alternaria leaf spot of cotton. The machine visualization system right now is usually contained on computer, digital camera and application software. Various kinds of algorithms are incorporated in the application software. Image processing analysis is one significant method that helps to segment the image into objects and background. One of the key steps in image analysis is feature detection.Plant disease reduces the production and quality of food, fiber and biofuel crops. Image processing has fascinated many researchers in the area of pattern recognition and technique combined with data mining are applied to trace out the leaf spots from the of plant leaves, which helps in diagnosing the cotton leaf spot diseases accurately The image processing techniques are extensively applied to agricultural science, and it has great perspective, especially in the plant protection field, which ultimately leads to crop management. Image analysis can be applied for the following purposes: 1. To detect diseased leaf, stem, fruit, root 2. To enumerate affected area by disease. 3. To find the boundaries of the affected area. A moreover leaf spot is a major component of the crops [2]. The diseases can be easily recognized with the help of the polluted vicinity of the crop. Usually the leaves will naturally going to focus the impure part in a clear way which can be easily identified. Generally by naked eyes we can easily identify the infected region. So we can say the transform in the crop color is the imperative feature for the notification. When the health of the crop is in good stage then the color of the crop is dissimilar but as soon as the crop is going to affected by some harming pathogens, the color transforms automatically. Crop diseases have turned into a dilemma because it may cause a diminution in productivity [3]. In this research work described that goal of identifying foliar diseases in cotton plantations. The primary goal of the developed system has been to identify the existence of pathogens in cotton fillers. Once a disease is identified it has to be automatically classified through further processing of the corresponding image. A survey conducted in one of south zone particular Tamil Nadu at Andhiyur district. During the investigation congregate from the farmers' side gathered the suggestion about the cotton crop diseases details. In this work organized about the Section 2. Literature survey, Section 3. Motivation, Problem Analysis, Section 4. Material and Methods, Section 5. Comparative studies, Section 6. Result and Discussion, Section 7. Conclusion and Acknowledgment, References. II. Literature Survey The proposed work describes about the diagnosis of cotton leaves using various approaches suggesting that the various implementation ways as illustrated and discussed below. Hui Li et al., in the year 2011 has been implemented the Web-Based Intelligent Diagnosis System for Cotton Diseases Control system the author proposed a BP neural network for his system. International Journal of Scientific Engineering and Technology (ISSN : 2277-1581) Volume No.3, Issue No.1 pp : 22-30 1 Jan 2014 IJSET@2014 Page 23 A research scheme was designed for the system test, in which 80 samples, including 8 main species of diseases, 10 samples in each sort were included. The result showed the rate of correctness that system could identify the symptom was 89.5% in average, and the average running time for a diagnosis was 900ms [4].Yan Cheng Zhang et al., ( 2007 ) proposed fuzzy feature selection approach fuzzy curves (FC) and fuzzy surfaces (FS) – to select features and classification of cotton disease levels. [5]. Syed A. Health et al., In this research work discussed about the automated system that can identify the pest and disease affecting parts such as cotton leaf, boll or flower. In this work proposed a CMYK based image cleaning technique to remove shadows, hands and other impurities from images. The outcomes are tested over a database consisting of 600 images to classify the presented image as a leaf, boll or flower [6]. Bernardes A. A. et al., ( 2011) proposed method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while SVM has been used for classification. The image set of supposedly adulterated leaves was classified within one of the four other sub-classes, namely: MA, RA, AS, and NONE. Finally obtained were: 96.2% accuracy for the SA class, 97.1% accuracy for the MA class, 80% accuracy for the RA class, and 71.4% accuracy for the AS class [7]. Meunkaewjinda. A, et al., (2008). In his work the cotton leaf disease segmentation is performed using modified self organizing feature map with genetic algorithms for optimization and support vector machines for classification. Finally, the resulting segmented image is filtered by Gabor wavelet which allows the system to analyze leaf disease color features more efficiently [8]. Gulhane. V. A et al. (2011) This work described Self organizing feature map together with a back-propagation neural network is used to recognize the color of the image. Finally find out the classification of the cotton diseases [9]. Viraj A. Gulhane. e tal., (2012).This proposed work addresses about the disease analysis is possible for the cotton leaf disease recognition, the analysis of the various diseases present on the cotton leaves can be effectively detected in the early stage before it will injure the whole crops, initially we can be able to detect three types of diseases of the cotton leaves by the methodology of Eigen feature regularization and extraction technique. In this method 90% of detection of Red spot i.e. fungal disease is detected, it is most dangerous disease; it can highly affect the productivity of the cotton crop in more extent. And if it detects in early stage we can say that, we able to make better manufacture [10]. Qinghai He et al., (2013). In this paper the author described RGB color model, HIS color model, and YCbCr color model for extracting the injured image from cotton leaf images were developed.. The ratio of damage (γ) was chosen as feature to measure the degree of damage which caused by diseases or pests. In this work shows the comparison of the results obtained by implementing in different color models, the comparison of outcomes shows good accuracy in both color models and YCbCr color space is considered as the best color model for extracting the damaged image [11]. III. Motivation This research work address on solving various problems like boosting up the precision rates and diminish the error rates using the Proposed Enhanced PSO which make use of Skew divergences to calculate the features correctly from the image.This method increases the accuracy and reduce the information gap to farmers. Creating a complicated agricultural environment to support the farmers to easily identify the diseases and get control of it using the Pest Recommendation module.  Deployed the automatic disease classification processes using advanced Data mining and Image processing techniques.  Augment the profit and safety of the farmer’s life and diminish their burden.  Time consuming (less).  Enhance the economic status of our country. Problem Credentials  Ancient days crop disease identification process through the laboratory condition.  However, this requires continuou

negative: A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.

negative: Association Rule Mining: A Survey Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be

negative: 3D human face description: landmarks measures and geometrical features Morphometric measures and geometrical features are widely used to descript faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their charateristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various morphometric measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.

negative: Spatial Data Mining Features between General Data Mining Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.