Query: Fruit and Vegetable Identification Using Machine Learning

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Heuristic evaluation of user interfaces <=SEP=> Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four experiments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found between 20 and 51% of the usability problems in the interfaces they evaluated. On the other hand, we could aggregate the evaluations from several evaluators to a single evaluation and such aggregates do rather well, even when they consist of only three to five people.

ImageNet: A large-scale hierarchical image database <=SEP=> 

Convolutional networks for images, speech, and time series <=SEP=> 

Stochastic Gradient Descent Tricks <=SEP=> Chapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique called stochastic gradient descent (SGD). This chapter provides background material, explains why SGD is a good learning algorithm when the training set is large, and provides useful recommendations.

Introduction to React <=SEP=> Introduction to React teaches you React, the JavaScript framework created by developers at Facebook, to solve the problem of building complex user interfaces in a consistent and maintainable way. React.js shrugs away common front-end conventions in an effort to make things more effi cient use Introduction to React to learn about this framework and more today. Get to know the React API and it’s specifi c JavaScript extension, JSX, which makes authoring React components easier and maintainable. You will also learn how to test your React applications and about the tools you can use while building. Once you understand these core concepts, you can build applications with React. This will help you cement the ideas and fundamentals of React and prepare you to utilize React in your own use case.

Wearable driver drowsiness detection using electrooculography signal <=SEP=> Every year, more than 100,000 automobile crashes are caused by driver drowsiness. Various technologies have been developed to address this issue, including vehicle-based measurements, behavior change detection, and physiological signal analysis. Both vehicle-based measurements and behavior change detection require bulky components. They also identify the driver's drowsiness too late for effective accident prevention. The physiological signal changes in an early stage and can be used to detect the on-set of driver drowsiness. In this paper, the development of a wearable drowsiness detection system is introduced. This system measures the electrooculography (EOG) signal; transmits the signal to a smartphone wirelessly; and could alarm the driver based on a prediction algorithm that can estimate 0.5-second-ahead EOG signal behavior. This system is compact, comfortable, and cost effective. The 0.5-second-ahead estimation capability provides the critical time for a driver to correct the behavior, and ultimately saves lives.

Excipient-drug interactions in parenteral formulations. <=SEP=> Excipients are added to parenteral formulations to enhance or maintain active ingredient solubility (solubilizers) and/or stability (buffers, antioxidants, chelating agents, cryo- and lyoprotectants). Excipients also are important in parenteral formulations to assure safety (antimicrobial preservatives), minimize pain and irritation upon injection (tonicity agents), and control or prolong drug delivery (polymers). These are all examples of positive or synergistic interactions between excipients and drugs. However, excipients may also produce negative effects such as loss of drug solubility, activity, and/or stability. This review article will highlight documented interactions, both synergistic and antagonistic, between excipients and drugs in parenteral formulations. The reader will gain better understanding and appreciation of the implications of adding formulation ingredients to parenteral drug products.

Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing <=SEP=> CSCW researchers have become interested in crowd work as a new form of collaborative engagement, that is, as a new way in which peopleâs actions are coordinated in order to achieve collective effects. We address this area but from a different perspective â that of the labor practices involved in taking crowd work as a form of work. Using empirical materials from a study of ride-sharing, we draw inspiration from studies of the immaterial forms of labor and alternate analyses of political economy that can cast a new light on the context of crowd labor that might matter for CSCW researchers.

Social Media and Information Overload: Survey Results <=SEP=> A UK-based online questionnaire investigating aspects of usage of usergenerated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy. Keyword list: User-Generated Media, Social Networking, Information Overload, Information Overlook, Facebook, LinkedIn, Twitter

Dynamic Influence Analysis in Evolving Networks <=SEP=> We propose the first real-time fully-dynamic index data structure designed for influence analysis on evolving networks. With this aim, we carefully redesign the data structure of the state-of-the-art sketching method introduced by Borgs et al., and construct corresponding update algorithms. Using this index, we present algorithms for two kinds of queries, influence estimation and influence maximization, which are strongly motivated by practical applications, such as viral marketing. We provide a thorough theoretical analysis, which guarantees the non-degeneracy of the solution accuracy after an arbitrary number of updates. Furthermore, we introduce a reachability-tree-based technique and a skipping method, which greatly reduce the time consumption required for edge/vertex deletions and vertex additions, respectively, and counter-based random number generators, which improve the space efficiency. Experimental evaluations using real dynamic networks with tens of millions of edges demonstrate the efficiency, scalability, and accuracy of our proposed indexing scheme. Specifically, it can reflect a graph modification within a time of several orders of magnitude smaller than that required to reconstruct an index from scratch, estimate the influence spread of a vertex set accurately within a millisecond, and select highly influential vertices at least ten times faster than state-of-the-art static algorithms.

Improving personalized web search using result diversification <=SEP=> We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods.

The TNF and TNF Receptor Superfamilies Integrating Mammalian Biology <=SEP=> The receptors and ligands in this superfamily have unique structural attributes that couple them directly to signaling pathways for cell proliferation, survival, and differentiation. Thus, they have assumed prominent roles in the generation of tissues and transient microen-capabilities are crucial in coordinating the proliferation and protective functions of pathogen-reactive cells. Here, we review the organization of the TNF/TNFR SF and how these proteins have been adapted for pro-Bethesda, Maryland 20892 cesses as seemingly disparate as host defense and or-ganogenesis. In interpreting this large and highly active area of research, we have focused on common themes Introduction that unite the actions of these genes in different tissues. We also discuss the evolutionary success of this super-Three decades ago, lymphotoxin (LT) and tumor necro-sis factor (TNF) were identified as products of lympho-family—success that we infer from its expansion across the mammalian genome and from its many indispens-cytes and macrophages that caused the lysis of certain types of cells, especially tumor cells (Granger et al., able roles in mammalian biology.The normal functions of TNF/TNFR SFPs, as well as ally, it became clear that they were members of a gene certain diseases involving them, depend on the obliga-superfamily. Not surprisingly, the receptors for these tory 3-fold symmetry that defines the essential signaling proteins also constitute a TNF receptor (TNFR)-related stoichiometry and structure (Figure 1). The ligands are gene superfamily. Large-scale sequencing of " ex-type 2 proteins that can have both membrane-embed-pressed sequence tags " (ESTs) identified many related ded " pro " as well as cleaved, soluble " mature " forms proteins, collectively referred to here as TNF-and TNFR-(for review, see Idriss and Naismith, 2000). Both forms related superfamily proteins (TNF/TNFR SFPs; reviewed are active as self-assembling noncovalent trimers, whose individual chains fold as compact " jellyroll " ␤ sandwiches and interact at hydrophobic interfaces (Fesik, UCL.ac.uk/users/hester/tnfinfo.html). The familiar as 2000) (Figure 1A). The 25%–30% amino acid similarity well as standardized names of these proteins are listed between TNF-like ligands is largely confined to internal in Table 1, together with their gene locations, pheno-aromatic residues responsible for trimer assembly. The types caused by mutations in these genes, and identified external surfaces of ligand trimers show little sequence functions. similarity, which accounts for receptor selectivity (Figure The discovery that cachectin, a protein known to 2). The ligand shape is that of an inverted bell that is cause fever and wasting, was identical to TNF provided …

A Double-Sided LCLC-Compensated Capacitive Power Transfer System for Electric Vehicle Charging <=SEP=> A double-sided LCLC-compensated capacitive power transfer (CPT) system is proposed for the electric vehicle charging application. Two pairs of metal plates are utilized to form two coupling capacitors to transfer power wirelessly. The LCLC-compensated structure can dramatically reduce the voltage stress on the coupling capacitors and maintain unity power factor at both the input and output. A 2.4-kW CPT system is designed with four 610-mm × 610-mm copper plates and an air gap distance of 150 mm. The experimental prototype reaches a dc-dc efficiency of 90.8% at 2.4-kW output power. At 300-mm misalignment case, the output power drops to 2.1 kW with 90.7% efficiency. With a 300-mm air gap distance, the output power drops to 1.6 kW with 89.1% efficiency.

Depth of field rendering via adaptive recursive filtering <=SEP=> We present a new post-processing method for rendering high-quality depth-of-field effects in real time. Our method is based on a recursive filtering process, which adaptively smooths the image frame with local depth and circle of confusion information. Unlike previous post-filtering approaches that rely on various convolution kernels, the behavior of our filter is controlled by a weighting function defined between two neighboring pixels. By properly designing this weighting function, our method produces spatially-varying smoothed results, correctly handles the boundaries between in-focus and out-of-focus objects, and avoids rendering artifacts such as intensity leakage and blurring discontinuity. Additionally, our method works on the full frame without resorting to image pyramids. Our algorithms runs efficiently on graphics hardware. We demonstrate the effectiveness of the proposed method with several complex scenes.

BAP: A Binary Analysis Platform <=SEP=> BAP is a publicly available infrastructure for performing program verification and analysis tasks on binary (i.e., executable) code. In this paper, we describe BAP as well as lessons learned from previous incarnations of binary analysis platforms. BAP explicitly represents all side effects of instructions in an intermediate language (IL), making syntaxdirected analysis possible. We have used BAP to routinely generate and solve verification conditions that are hundreds of megabytes in size and encompass 100,000’s of assembly instructions.

Learning feed-forward one-shot learners <=SEP=> One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark.

A Study of Big Data in Cloud Environment with their Related Challenges <=SEP=> Big Data , it is a word which is used for the huge amount of data sets which have the big, extra various and difficu lt structure facing some difficult ies in storing the data then analyzing and visualizing the data for the final outcome [1] . Big data analytics is the procedure of probing huge amounts of data. Here we use cloud computing technology so firstly acquaint the general environment of big data and some associated technology like Hadoop. This powerful technology is used to execute the huge s cale and complicated data computing. It eradicates the requirements of hardware computing which is costly dedicated space and software’s [3]. It is a resolution for analyzing Big Data by deploying Big Data softwares like Hadoop on cloud background which gives users an innovative pattern of service called as Analytics as a Service [7]. Hadoop uses the map-reduce paradigm .We also talk about various advantages, challenges, and issues associated with Big Data.

Online human gesture recognition from motion data streams <=SEP=> Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.

On Covering a Solid Sphere with Concentric Spheres in ${\mathbb Z}^3$ <=SEP=> We show that a digital sphere, constructed by the circular sweep of a digital semicircle (generatrix) around its diameter, consists of some holes (absentee-voxels), which appear on its spherical surface of revolution. This incompleteness calls for a proper characterization of the absentee-voxels whose restoration will yield a complete spherical surface without any holes. In this paper, we present a characterization of such absentee-voxels using certain techniques of digital geometry and show that their count varies quadratically with the radius of the semicircular generatrix. Next, we design an algorithm to fill these absentee-voxels so as to generate a spherical surface of revolution, which is more realistic from the viewpoint of visual perception. We further show that covering a solid sphere by a set of complete spheres also results in an asymptotically larger count of absentees, which is cubic in the radius of the sphere. The characterization and generation of complete solid spheres without any holes can also be accomplished in a similar fashion. We furnish test results to substantiate our theoretical findings.

Automatic Text Summarization System for Punjabi Language <=SEP=> This paper concentrates on single document multi news Punjabi extractive summarizer. Although lot of research is going on in field of multi document news summarization systems but not even a single paper was found in literature for single document multi news summarization for any language. It is first time that this system has been developed for Punjabi language and is available online at: http://pts.learnpunjabi.org/. Punjab is one of Indian states and Punjabi is its official language. Punjabi is under resourced language. Various linguistic resources for Punjabi were also developed first time as part of this project like Punjabi noun morph, Punjabi stemmer and Punjabi named entity recognition, Punjabi keywords identification, normalization of Punjabi nouns etc. A Punjabi document (like single page of Punjabi E-news paper) can have hundreds of multi news of varying length. Based on compression ratio selected by user, this system starts by extracting headlines of each news, lines just next to headlines and other important lines depending upon their importance. Selection of sentences is on the basis of statistical and linguistic features of sentences. This system comprises of two main steps: Pre Processing and Processing phase. Pre Processing phase represents the Punjabi text in structured way. In processing phase, different features deciding the importance of sentences are determined and calculated. Some of the statistical features are Punjabi keywords identification, relative sentence length feature and numbered data feature. Various linguistic features for selecting important sentences in summary are: Punjabiheadlines identification, identification of lines just next to headlines, identification of Punjabi-nouns, identification of Punjabi-proper-nouns, identification of common-EnglishPunjabi-nouns, identification of Punjabi-cue-phrases and identification of title-keywords in sentences. Scores of sentences are determined from sentence-feature-weight equation. Weights of features are determined using mathematical regression. Using regression, feature values of some Punjabi documents which are manually summarized are treated as independent input values and their corresponding dependent output values are provided. In the training phase, manually summaries of fifty newsdocuments are made by giving fuzzy scores to the sentences of those documents and then regression is applied for finding values of feature-weights and then average values of feature-weights are calculated. High scored sentences in proper order are selected for final summary. In final summary, sentences coherence is maintained by properly ordering the sentences in the same order as they appear in the input text at the selective compression ratios. This extractive Punjabi summarizer is available online.

Calibration of Ultrawide Fisheye Lens Cameras by Eigenvalue Minimization <=SEP=> We present a new technique for calibrating ultrawide fisheye lens cameras by imposing the constraint that collinear points be rectified to be collinear, parallel lines to be parallel, and orthogonal lines to be orthogonal. Exploiting the fact that line fitting reduces to an eigenvalue problem in 3D, we do a rigorous perturbation analysis to obtain a practical calibration procedure. Doing experiments, we point out that spurious solutions exist if collinearity and parallelism alone are imposed. Our technique has many desirable properties. For example, no metric information is required about the reference pattern or the camera position, and separate stripe patterns can be displayed on a video screen to generate a virtual grid, eliminating the grid point extraction processing.

An Approach to Discovering Temporal Association Rules <=SEP=> The goal of discovering association rules is to discover aH possible associations that accomplish certain restrictions (minimum support and confidence and interesting). However, it is possible to find interesting associations with a high confidence level hut with little support. This problem is caused by the way support is calculated, as the denominator represents the total number of transactions in a time period when the involved items may have not existed. If, on the other hand, we limit the total transactions to the ones belonging to the items' lifetime, those associations would be now discovered, as they would count on enough support. Another difficulty is the large number of rules that could be generated, tbr which many solutions have been proposed. Using age as an obsolescence tactor for rules helps reduce the number of rules to be presented to the user. In this paper we expand the notion of association rules incorporating time to the frequent itemsets discovered. The concept of temporal support is introduced and. as an example, the known algorithm A priori is modified to incorporate the temporal notions.

Liquid Biopsy in Liquid Tumors <=SEP=> The availability of a minimally invasive patient simple, capable of providing tumor information, represents a valuable clinical tool. The liquid biopsy has the potential to achieve this need. Circulating cell free DNA (ccfDNA), other circulating nucleic acids such as microRNA and circulating tumor cells (CTCs), can be obtained from a peripheral blood sample. Liquid biopsy has been particularly studied in solid tumors, specially of the epitelial origin, such as a pancreatic carcinoma and advanced breast cancer. It has been considerably less applied to the study of non-solid tumors. It represents an important source for diagnosis, prognosis and predictive information. Also it is suitable to evaluate response to therapy and drugs pharmacokinetics. It provides a unique opportunity to evaluate the disease evolution in serial blood samples collection, otherwise difficult to obtain. Liquid biopsy can be rehearsed using different circulating biological fluids such as whole blood, serum, plasma and lymph, as well as, non-circulating fluids such as urine, feces, saliva, bile and accumulated pathological fluids such as ascites. This review summarizes the current status of circulating material analysis in non-solid tunors. It is specially focused on Hodgkin Lymphoma and among Non-Hodgkin Lymphoma, it refers particularly to Diffuse Large B cell Lymphoma, the most common aggressive Non-Hodgkin Lymphoma derived from germinal center B-cells in adults. It further discusses the benefit of liquid biopsy in oncohemtaological diseases and potential clinical applications.

PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks <=SEP=> Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

An ANN-Based Synthesis Model for the Single-Feed Circularly-Polarized Square Microstrip Antenna With Truncated Corners <=SEP=> An artificial neural network-based synthesis model is proposed for the design of single-feed circularly-polarized square microstrip antenna (CPSMA) with truncated corners. To obtain the training data sets, the resonant frequency and Q-factor of square microstrip antennas are calculated by empirical formulae. Then the size of the truncated corners and the operation frequency with the best axial ratio are obtained. Using the Levenberg-Marquardt (LM) algorithm, a three hidden layered network is trained to achieve an accurate synthesis model. At last, the model is validated by comparing its results with the electromagnetic simulation and measurement. It is extremely useful to antenna engineers for directly obtaining patch physical dimensions of the single-feed CPSMA with truncated corners.

Infrapatellar saphenous neuralgia - diagnosis and treatment. <=SEP=> Persistent anterior knee pain, especially after surgery, can be very frustrating for the patient and the clinician. Injury to the infrapatellar branch of the saphenous nerve (IPS) is not uncommon after knee surgeries and trauma, yet the diagnosis and treatment of IPS neuralgia is not usually taught in pain training programs. In this case report, we describe the anatomy of the saphenous nerve and specifically the infrapatellar saphenous nerve branch; we also discuss the types of surgical trauma, the clinical presentation, the diagnostic modalities, the diagnostic injection technique, and the treatment options. As early as 1945, surgeons were cautioned regarding the potential surgical trauma to the IPS. Although many authors dismissed the nerve damage as unavoidable, the IPS is now recognized as a potential cause of persistent anterior and anteriomedial knee pain. Even more concerning, damage to peripheral nerves such as the IPS has been identified as the cause and potential perpetuating factor for conditions such as complex regional pain syndromes (CRPS). Because the clinical presentation may be vague, it has often been misdiagnosed and underdiagnosed. There is a documented vasomotor instability, but, unfortunately, sympathetic blocks will not address the underlying pathology, and therefore patients often will not respond to this modality, although the correct diagnosis can lead to rapid and gratifying resolution of the pathology. An entity unknown to the clinician is never diagnosed, and so it is important to familiarize pain physicians with IPS neuropathy so that they may be able to offer assistance when this painful condition arises.

The Timbre Model <=SEP=> This paper presents the timbre model, a signal model which has been built to better understand the relationship between the perception of timbre and the musical sounds most commonly associated with timbre. In addition, an extension to the timbre model incorporating expressions is introduced. The presented work therefore has relation to a large field of science, including auditory perception, signal processing, physical models and the acoustics of musical instruments, music expression, and other computer music research. The timbre model is based on a sinusoidal model, and it consists of a spectral envelope, frequencies, a temporal envelope and different irregularity parameters. The paper is divided into four parts: an overview of the research done on the perception of timbre, an overview of the signal processing aspects dealing with sinusoidal modeling, the timbre model, and an introduction of some expressive extensions to the timbre model.

Synthetic aperture radar interferometry-Invited paper <=SEP=> 

BreadCrumbs: forecasting mobile connectivity <=SEP=> Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage.
 To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.

Tracking and Connecting Topics via Incremental Hierarchical Dirichlet Processes <=SEP=> Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.