Setting Policy Priorities for the Development of Tree Crop Industries in Papua New Guinea

Crop Production/Industries,

The Diagnosis of Dyspepsia in General Practice with Notes on a Simple Method of Classification

In the Introduction we defined dyspepsia so as to include all cases, functional or organic, in which discomfort of any kind occurred during digestion; and we stated our object as follows: (1) To formulate a routine procedure, suitable for General Practice, in the examination and diagnosis of cases of dyspepsia. (2) To classify the various kinds of dyspepsia in a way which shall be useful and simple; and which shall describe each condition adequately for the purpose of rational treatment. (5) To examine the results in 170 cases. In Chapter Three we have explained our procedure. This is more or less in accordance with convention as regards the history, interrogation and examination, i.e. the purely clinical part. That part must be done as fully in General Practice as in hospital work and in it there is no curtailment desirable or justifiable. It is in regard to Special Methods (more particularly the test-meal, the test for occult blood in the stools and the use of X-rays) that we have endeavoured to discriminate and decide in which forms of dyspepsia one or more of these is necessary. The conclusions reached are referred to in detail under the sections on the various diseases. We have made a classification of dyspepsia which seems to us to fulfil the purpose we have indicated above. The difficulties have been discussed and reasons advanced for the arrangement we have formulated in Chapter Four. In the second Volume notes of 170 cases have been included. A few conclusions and comments regarding these are given at the end of the Volume. The case-sheets are constructed on the lines indicated in our routine procedure. It is not to be supposed that these notes contain all that was used in the diagnosis. They contain the salient features and, we trust, make clear the method by which we investigate a suspected case of dyspepsia. Much that is important has of necessity been excluded on account of exigencies of space. For example, the examiner's impression of the patient's temperament has often been summed up by such a word as "nervous." To deal adequately with questions of this nature is impossible in tabular notes such as we have been obliged to use. It is apparent however that they may be of considerable importance in the diagnosis

Multi-scale, class-generic, privacy-preserving video

In recent years, high-performance video recording devices have become ubiquitous, posing an unprecedented challenge to preserving personal privacy. As a result, privacy-preserving video systems have been receiving increased attention. In this paper, we present a novel privacy-preserving video algorithm that uses semantic segmentation to identify regions of interest, which are then anonymized with an adaptive blurring algorithm. This algorithm addresses two of the most important shortcomings of existing solutions: it is multi-scale, meaning it can identify and uniformly anonymize objects of different scales in the same image, and it is class-generic, so it can be used to anonymize any class of objects of interest. We show experimentally that our algorithm achieves excellent anonymity while preserving meaning in the visual data processed

SemanticLock: An authentication method for mobile devices using semantically-linked images

We introduce SemanticLock, a single factor graphical authentication solution for mobile devices. SemanticLock uses a set of graphical images as password tokens that construct a semantically memorable story representing the user`s password. A familiar and quick action of dragging or dropping the images into their respective positions either in a \textit{continous flow} or in \textit{discrete} movements on the the touchscreen is what is required to use our solution. The authentication strength of the SemanticLock is based on the large number of possible semantic constructs derived from the positioning of the image tokens and the type of images selected. Semantic Lock has a high resistance to smudge attacks and it equally exhibits a higher level of memorability due to its graphical paradigm. In a three weeks user study with 21 participants comparing SemanticLock against other authentication systems, we discovered that SemanticLock outperformed the PIN and matched the PATTERN both on speed, memorability, user acceptance and usability. Furthermore, qualitative test also show that SemanticLock was rated more superior in like-ability. SemanticLock was also evaluated while participants walked unencumbered and walked encumbered carrying "everyday" items to analyze the effects of such activities on its usage

Remote functionalisation via sodium alkylamidozincate intermediates : access to unusual fluorenone and pyridyl ketone reactivity patterns

Treating fluorenone or 2-benzoylpyridine with the sodium zincate [(TMEDA)center dot Na(mu-Bu-t)(mu-TMP)Zn(Bu-t)] in hexane solution, gives efficient Bu-t addition across the respective organic substrate in a highly unusual 1,6-fashion, producing isolable organometallic intermediates which can be quenched and aerobically oxidised to give 3-tert-butyl-9H-fluoren-9-one and 2-benzoyl-5-tert-butylpyridine respectively

Biomove: Biometric user identification from human kinesiological movements for virtual reality systems

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (\u3c50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems

Biomove: Biometric user identification from human kinesiological movements for virtual reality systems

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (\u3c50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems

Scale Invariant Privacy Preserving Video via Wavelet Decomposition

Video surveillance has become ubiquitous in the modern world. Mobile devices, surveillance cameras, and IoT devices, all can record video that can violate our privacy. One proposed solution for this is privacy-preserving video, which removes identifying information from the video as it is produced. Several algorithms for this have been proposed, but all of them suffer from scale issues: in order to sufficiently anonymize near-camera objects, distant objects become unidentifiable. In this paper, we propose a scale-invariant method, based on wavelet decomposition