title: Probabilistic system-on-a-chip architectures

abstract: Parameter variations, noise susceptibility, and increasing energy dissipation of cmos devices have been recognized as major challenges in circuit and microarchitecture design in the nanometer regime. Among these, parameter variations and noise susceptibility are increasingly causing cmos devices to behave in an “unreliable” or “probabilistic” manner. To address these challenges, a shift in design paradigm from current-day deterministic designs to “statistical” or “probabilistic” designs is deemed inevitable. To respond to this need, in this article, we introduce and study an entirely novel family of probabilistic architectures: the probabilistic system-on-a-chip (psoc). psoc architectures are based on cmos devices rendered probabilistic due to noise, referred to as probabilistic CMOS or PCMOS devices. We demonstrate that in addition to harnessing the probabilistic behavior of pcmos devices, psoc architectures yield significant improvements, both in energy consumed as well as performance in the context of probabilistic or randomized applications with broad utility. All of our application and architectural savings are quantified using the product of the energy and performance, denoted (energy × performance): The pcmos-based gains are as high as a substantial multiplicative factor of over 560 when compared to a competing energy-efficient cmos-based realization. Our architectural design is application specific and involves navigating design space spanning the algorithm (application), its architecture (psoc), and the probabilistic technology (pcmos).


Measurement of plasma and tissue levels of branched-chain alpha-keto acids by gas-liquid chromatography.


title: Taxonomy-driven computation of product recommendations

abstract: Recommender systems have been subject to an enormous rise in popularity and research interest over the last ten years. At the same time, very large taxonomies for product classification are becoming increasingly prominent among e-commerce systems for diverse domains, rendering detailed machine-readable content descriptions feasible. Amazon.com makes use of an entire plethora of hand-crafted taxonomies classifying books, movies, apparel, and various other goods. We exploit such taxonomic background knowledge for the computation of personalized recommendations. Hereby, relationships between super-concepts and sub-concepts constitute an important cornerstone of our novel approach, providing powerful inference opportunities for profile generation based upon the classification of products that customers have chosen. Ample empirical analysis, both offline and online, demonstrates our proposal's superiority over common existing approaches when user information is sparse and implicit ratings prevail.


title: On model selection consistency of lasso

abstract: Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection. Therefore it is important to study Lasso for model selection purposes. ::: ::: In this paper, we prove that a single condition, which we call the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large. Based on these results, sufficient conditions that are verifiable in practice are given to relate to previous works and help applications of Lasso for feature selection and sparse representation. ::: ::: This Irrepresentable Condition, which depends mainly on the covariance of the predictor variables, states that Lasso selects the true model consistently if and (almost) only if the predictors that are not in the true model are "irrepresentable" (in a sense to be clarified) by predictors that are in the true model. Furthermore, simulations are carried out to provide insights and understanding of this result.


title: Data Leakage Detection

abstract: We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody's laptop). The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party.


title: Metacognition in human decision-making: confidence and error monitoring

abstract: People are capable of robust evaluations of their decisions: they are often aware of their mistakes even without explicit feedback, and report levels of confidence in their decisions that correlate with objective performance. These metacognitive abilities help people to avoid making the same mistakes twice, and to avoid overcommitting time or resources to decisions that are based on unreliable evidence. In this review, we consider progress in characterizing the neural and mechanistic basis of these related aspects of metacognition—confidence judgements and error monitoring—and identify crucial points of convergence between methods and theories in the two fields. This convergence suggests that common principles govern metacognitive judgements of confidence and accuracy; in particular, a shared reliance on post-decisional processing within the systems responsible for the initial decision. However, research in both fields has focused rather narrowly on simple, discrete decisions—reflecting the correspondingly restricted focus of current models of the decision process itself—raising doubts about the degree to which discovered principles will scale up to explain metacognitive evaluation of real-world decisions and actions that are fluid, temporally extended, and embedded in the broader context of evolving behavioural goals.


title: A study on the scale-up of acyl azide synthesis in various continuous flow reactors in homogeneous and biphasic systems

abstract: This article illustrates the feasibility of reaction scale up for the synthesis of various acyl azides in continuous flow systems, by increasing the channel diameter and mass transfer performance of different reactor configurations. When homogeneous conditions were used scale up in all systems was trivial, however when a biphasic reaction was evaluated the reactor configuration had a massive influence of reaction efficiency as a result of mass transfer. After optimisation, full conversion of various acyl azides was obtained with throughputs of 258–282 g/h.


title: Wideband Circularly Polarized Aperture-Fed Rotated Stacked Patch Antenna

abstract: A novel aperture stacked patch (ASP) antenna with circular polarization is proposed. The antenna consists of four parasitic patches, each one being rotated by an angle of 30° relative to its adjacent patches. The proposed antenna has achieved a simultaneous axial ratio <;3 dB and voltage standing wave ratio (VSWR) <;2 bandwidth of 33.6% (7.2-10.11 GHz) in the single element and 36.15% (7.1-10.2 GHz) in a 2 × 1-element array configuration. The antenna behavior is explained by a thorough parameter study together with fabrication and measurement.


title: Rethinking Atrous Convolution for Semantic Image Segmentation

abstract: In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.


title: Sneak-Path Testing of Crossbar-Based Nonvolatile Random Access Memories

abstract: Emerging nonvolatile memory (NVM) technologies, such as resistive random access memories (RRAM) and phase-change memories (PCM), are an attractive option for future memory architectures due to their nonvolatility, high density, and low-power operation. Notwithstanding these advantages, they are prone to high defect densities due to the nondeterministic nature of the nanoscale fabrication. We examine the fault models and propose an efficient testing technique to test crossbar-based NVMs. The typical approach to testing memories entails testing one memory element at a time. This is time consuming and does not scale for the dense, RRAM or PCM-based memories. We propose a testing scheme based on “sneak-path sensing” to efficiently detect faults in the memory. The testing scheme uses sneak paths inherent in crossbar memories, to test multiple memory elements at the same time, thereby reducing testing time. We designed the design-for-test support necessary to control the number of sneak paths that are concurrently enabled; this helps control the power consumed during test. The proposed scheme enables and leverages sneak paths during test mode, while still maintaining a sneak path free crossbar during normal operation.


REINFORCEMENT LEARNING: AN INTRODUCTION by Richard S. Sutton and Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., 1998, xviii + 322 pp, ISBN 0-262-19398-1, (hardback, £31.95).


Nasal Valve Suspension: An Improved, Simplified Technique for Nasal Valve Collapse


title: Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I.

abstract: BACKGROUND ::: As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care. ::: ::: ::: METHODS ::: We reviewed 30,121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians. ::: ::: ::: RESULTS ::: Adverse events occurred in 3.7 percent of the hospitalizations (95 percent confidence interval, 3.2 to 4.2), and 27.6 percent of the adverse events were due to negligence (95 percent confidence interval, 22.5 to 32.6). Although 70.5 percent of the adverse events gave rise to disability lasting less than six months, 2.6 percent caused permanently disabling injuries and 13.6 percent led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi 2 = 21.04, P less than 0.0001). Using weighted totals, we estimated that among the 2,671,863 patients discharged from New York hospitals in 1984 there were 98,609 adverse events and 27,179 adverse events involving negligence. Rates of adverse events rose with age (P less than 0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (P less than 0.01). There were significant differences in rates of adverse events among categories of clinical specialties (P less than 0.0001), but no differences in the percentage due to negligence. ::: ::: ::: CONCLUSIONS ::: There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.


title: Malware detection and classification based on extraction of API sequences

abstract: With the substantial growth of IT sector in the 21st century, the need for system security has also become inevitable. While the developments in the IT sector have innumerable advantages but attacks on websites and computer systems are also increasing relatively. One such attack is zero day malware attack which poses a great challenge for the security testers. The malware pen testers can use bypass techniques like Compression, Code obfuscation and Encryption techniques to easily deceive present day Antivirus Scanners. This paper elucidates a novel malware identification approach based on extracting unique aspects of API sequences. The proposed feature selection method based on N grams and odds ratio selection, capture unique and distinct API sequences from the extracted API calls thereby increasing classification accuracy. Next a model is built by the classification algorithms using active machine learning techniques to categorize malicious and benign files.


title: Voice Onset Time and the Perception of Japanese Voicing Contrasts (清水克正教授退職記念号)

abstract: Much crosslinguistic research exists on the production and perception of voice onset time (VOT). However, most research on the perception of VOT uses synthetic stimuli instead of natural speech stimuli. Effects of synthetic speech on the perception of VOT are not known, but more research needs to be done to see if there are differences between perception using synthetic speech and perception using natural speech. This pilot study uses natural speech to investigate perception of Japanese VOT by Japanese listeners. Results clearly show that not just VOT, but other phonetic factors too must be responsible for differences in perception of the voicedvoiceless distinction in Japanese word-initial stop consonants.


title: Exoskeletal Force-Sensing End-Effectors With Embedded Optical Fiber-Bragg-Grating Sensors

abstract: Force sensing is an essential requirement for dexterous robot manipulation. We describe composite robot end-effectors that incorporate optical fibers for accurate force sensing and estimation of contact locations. The design is inspired by the sensors in arthropod exoskeletons that allow them to detect contacts and loads on their limbs. In this paper, we present a fabrication process that allows us to create hollow multimaterial structures with embedded fibers and the results of experiments to characterize the sensors and controlling contact forces in a system involving an industrial robot and a two-fingered dexterous hand. We also briefly describe the optical-interrogation method used to measure multiple sensors along a single fiber at kilohertz rates for closed-loop force control.


title: A fraud detection visualization system utilizing radial drawings and heat-maps

abstract: We present a prototype system developed in cooperation with a business organization that combines information visualization and pattern-matching techniques to detect fraudulent activity by employees. The system is built upon common fraud patterns searched while trying to detect occupational fraud suggested by internal auditors of a business company. The main visualization of the system consists of a multi-layer radial drawing that represents the activity of the employees and clients. Each layer represents a different examined pattern whereas heat-maps indicating suspicious activity are incorporated in the visualization. The data are first preprocessed based on a decision tree generated by the examined patterns and each employee is assigned a value indicating whether or not there exist indications of fraud. The visualization is presented as an animation and the employees are visualized one by one according to their severity values together with their related clients.


title: Finite-Element Analysis of an Axial Flux Hysteresis Motor Based on a Complex Permeability Concept Considering the Saturation of the Hysteresis Loop

abstract: Axial Structure of the hysteresis motors like the other axial types of electrical machines, have some outstanding characteristics regarding radial structures such as higher torque to weight ratio, excellent efficiency, adjustable air-gap, and balanced rotor-stator attractive forces. Since, the accurate measurement of the hysteresis machine characteristics by prototyping and testing is expensive and time consuming, in this paper the finite element analysis is adopted for the prediction and analysis of an axial flux hysteresis motor. The analysis is conducted based on the elliptical approximation of the hysteresis loop. Whereas, the elliptical approximation is not able to model the saturation of the hysteresis loop, an iteration based method is proposed to overcome this problem and increase the analysis accuracy. Finally, in order to investigate the proposed model accuracy, the device is prototyped whose experimental results agree well with those extracted from the analytical model.


title: Design and Testing of a Linear Generator for Wave-Energy Applications

abstract: A linear-generator topology is proposed for wave-energy applications. The main significance of the generator topology is that the relative position of the magnets, copper, and steels has been chosen so that there are no magnetic attraction forces between a stator and a permanent-magnet (PM) translator. The lack of magnetic forces and the modular nature of the generator topology make the manufacture and assembly of the generator easier than a conventional iron-cored PM linear generator. Analytical modeling techniques are described with a genetic-algorithm optimization method. The proposed topology is implemented to an Archimedes-wave-swing wave-energy converter. A 50-kW prototype has been built to prove the concept, and the no-load- and load-test results are presented.


title: Classifier technology and the illusion of progress. Statist

abstract: A great many tools have been developed for supervised clas- sification, ranging from early methods such as linear discriminant anal- ysis through to modern developments such as neural networks and sup- port vector machines. A large number of comparative studies have been conducted in attempts to establish the relative superiority of these methods. This paper argues that these comparisons often fail to take into account important aspects of real problems, so that the apparent superiority of more sophisticated methods may be something of an illu- sion. In particular, simple methods typically yield performance almost as good as more sophisticated methods, to the extent that the di!erence in performance may be swamped by other sources of uncertainty that generally are not considered in the classical supervised classification paradigm.


title: Iterative multimodel subimage binarization for handwritten character segmentation

abstract: Existing binarization methods are categorized as either global or local. In this paper, we present a new category, where the image is considered a collection of subimages. Each subimage provides a statistical model for the handwritten characters that can be used to optimize the binarization of other subimages based on gray-level and stroke-run features. The proposed method uses these multimodels to iteratively arrive at the optimal threshold for each subimage. It can be applied to different types of documents where prior knowledge about the noisiness of the subimages is not available. Experimental results showed significant improvement in the binarization quality in comparison with other well-established algorithms.


title: BIMQL - An open query language for building information models

abstract: In this paper we present the on-going development of a framework for a domain specific, open query language for building information models. The proposed query language is intended for selecting, updating and deleting of data stored in Industry Foundation Classes models. Even though some partial solutions already have been suggested, none of them are open source, domain specific, platform independent and implemented at the same time. This paper provides an overview of existing approaches, conceptual sketches of the language in development and documents the current state of implementation as a prototype plugin developed for the open source model server platform bimserver.org. We report on the execution of example test-cases to show the general feasibility of the approach chosen.


title: LARAe: Learning analytics reflection & awareness environment

abstract: Exploring and managing the abundance of data that Learning Analytics generate is a challenge for both teachers and students. This paper introduces a Learning Dashboard that provides an overview, context and content of learner traces to help students with awareness of feedback and progress, and assist teachers with monitoring student effort and outcomes to intervene where needed.


title: A VNF-as-a-service design through micro-services disassembling the IMS

abstract: With the evolution of Telco systems towards 5G, new requirements emerge for delivering services. Network services are expected to be designed to allow greater flexibility. In order to cope with the new users' requirements, Telcos should rethink their complex and monolithic network architectures into more agile architectures. Adoption of NFV as well as micro-services patterns are opportunities promising such an evolution. However, to gain in flexibility, it is crucial to satisfy structural requirements for the design of VNFs as services. We present in this paper an approach for designing VNF-asa-Service. With this approach, we define design requirements for the service architecture and the service logic of VNFs. As Telcos have adopted IMS as the de facto platform for service delivery in 3G and even 4G systems, it is interesting to study its evolution for 5G towards a microservices-based architecture with an optimal design. Therefore, we consider IMS as a case of study to illustrate the proposed approach. We present new functional entities for IMS-as-a-Service through a functional decomposition of legacy network functions. We have developed and implemented IMS-as-a-Service with respect to the proposed requirements. We consider a service scenario where we focus on authentication and authorization procedures. We evaluate the involved microservices comparing to the state-of-the-art. Finally, we discuss our results and highlight the advantages of our approach.


title: GEMs: shared-memory parallel programming for Node.js

abstract: JavaScript is the most popular programming language for client-side Web applications, and Node.js has popularized the language for server-side computing, too. In this domain, the minimal support for parallel programming remains however a major limitation. In this paper we introduce a novel parallel programming abstraction called Generic Messages (GEMs). GEMs allow one to combine message passing and shared-memory parallelism, extending the classes of parallel applications that can be built with Node.js. GEMs have customizable semantics and enable several forms of thread safety, isolation, and concurrency control. GEMs are designed as convenient JavaScript abstractions that expose high-level and safe parallelism models to the developer. Experiments show that GEMs outperform equivalent Node.js applications thanks to their usage of shared memory.


Reliability of the Shoulder Symptom Modification Procedure and association of within-session and between-session changes with functional outcomes


title: Clinician Guide to Microbiome Testing

abstract: Recent recognition that the intestinal microbiome plays potential roles in the pathogenesis of multiple common diseases has led to a growing interest in personalized microbiome analysis among clinical investigators and patients. Permissibility of direct access testing has allowed the emergence of commercial companies offering microbiome analysis to patients seeking to gain a better understanding of their symptoms and disease conditions. In turn, physicians are often asked to help with interpretation of such tests or even requested by their patients to order them. Therefore, physicians need to have a basic understanding of the current state of microbiome science. This review examines how the perspective of microbial ecology, which is fundamental to understanding the microbiome, updates the classical version of the germ theory of disease. We provide the essential vocabulary of microbiome science and describe its current limitations. We look forward to the future when microbiome diagnostics may live up to its potential of becoming integral to clinical care that will become increasingly individualized, and microbiome analysis may become incorporated into that future paradigm. However, we caution patients and providers that the current microbiome tests, given the state of knowledge and technology, do not provide much value in clinical decisions. Considerable research remains to be carried out to make this objective a reality.


title: Multi-objective convolutional learning for face labeling

abstract: This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm, and accurate labeling results on challenging images can be obtained by the proposed algorithm for real-world applications.


title: Contribution of diet to the composition of the human gut microbiota

abstract: In the human gut, millions of bacteria contribute to the microbiota, whose composition is specific for every individual. Although we are just at the very beginning of understanding the microbiota concept, we already know that the composition of the microbiota has a profound impact on human health. A key factor in determining gut microbiota composition is diet. Preliminary evidence suggests that dietary patterns are associated with distinct combinations of bacteria in the intestine, also called enterotypes. Western diets result in significantly different microbiota compositions than traditional diets. It is currently unknown which food constituents specifically promote growth and functionality of beneficial bacteria in the intestine. The aim of this review is to summarize the recently published evidence from human in vivo studies on the gut microbiota-modulating effects of diet. It includes sections on dietary patterns (e.g. Western diet), whole foods, food constituents, as wells as food-associated microbes and their influence on the composition of human gut microbiota. The conclusions highlight the problems faced by scientists in this fast-developing field of research, and the need for high-quality, large-scale human dietary intervention studies.


Reconciling self-protection with self-improvement: Self-affirmation theory.


title: Advances in Antenna Technology for Wireless Handheld Devices

abstract: The constant evolution of wireless handheld devices together with the apparition of multiple wireless communication systems fosters the antenna community to design new radiating and measurements systems capable of satisfying the market demands. It is an object of the present paper to provide an overview of the evolution that wireless handheld technology has experienced in the last years. In this sense, a description of the evolution of wireless handheld devices, regulations, challenges in today’s smartphones, and handset characterization is reviewed. Finally, recent advances in antenna technology for wireless handheld or portable devices are presented.


title: Model repair for probabilistic systems

abstract: We introduce the problem of Model Repair for Probabilistic Systems as follows. Given a probabilistic system M and a probabilistic temporal logic formula φ such that M fails to satisfy φ, the Model Repair problem is to find an M′ that satisfies v and differs from M only in the transition flows of those states in M that are deemed controllable. Moreover, the cost associated with modifying M's transition flows to obtain M′ should be minimized. Using a new version of parametric probabilistic model checking, we show how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique. We demonstrate the practical utility of our approach by applying it to a number of significant case studies, including a DTMC reward model of the Zeroconf protocol for assigning IP addresses, and a CTMC model of the highly publicized Kaminsky DNS cache-poisoning attack.


title: A 4-channel 24–27 GHz CMOS differential phased-array receiver

abstract: The paper presents a 24–27 GHz 4-channel CMOS differential phased array receiver front-end with integrated baluns, ESD protection and VGAs. The differential implementation of the phased array significantly reduces the substrate coupling effects. The measured array performance shows an input and output match of ≪ −10 dB, 12–15 dB gain, 7.8–9.5 dB NF, an IIP3 of −9 to −12 dBm, an input P 1dB of −19 to −22 dBm, and a 4-bit phase control with an rms gain and phase errors of ≪ 0.35 dB and ≪ 6°, respectively, all at 24–27 GHz. The measured rms gain and phase errors due to on-chip coupling effects are ≪ 0.25 dB and ≪ 2° at 20–30 GHz. The chip consumes 155 mA from a 1.5 V supply with an area of 4 mm2 and can operate over a 3 GHz instantaneous bandwidth (limited by the gain response).


title: Designing smart cities: security issues

abstract: Smart city means an intelligent city called post-ubiquitous city. However, to be a smart city, there are some issues to consider carefully. The first is the security issues having platform and intelligent surveillance function and analysis function as well as inference. The second is the governance issues between government and cities which are developing. And the third is the service issues to be realized in the cities. In this paper, we are mainly concentrated on the first security issue. Here we propose a speech recognition technology in emenrgency situation for secure cities. For the emergency detection in general CCTVs(closed circuit television) environment of our daily life, the monitoring by only images through CCTVs information occurs some problems especially in emergency state. Therefore for detecting emergency state dynamically through CCTVs as well as resolving some problems, we propose a detection and recognition method for emergency and non-emergency speech by Gaussian Mixture Models(GMM). The proposed method determines whether input speech is emergency or non-emergency speech by global GMM firstly. If this is an emergency speech, then local GMM is performed secondly to classify the type of emergency speech. The proposed method is tested and verified by emergency and non-emergency speeches in various environmental conditions. Also, we discuss about the platform issues having analysis and inference function of big data in smart city.


title: Hacktivism trends, digital forensic tools and challenges: A survey

abstract: Hacktivism is the biggest challenge being faced by the Cyber world. Many digital forensic tools are being developed to deal with this challenge but at the same pace hackers are developing the counter techniques. This paper includes the digital forensics basics along with the recent trends of hacktivism in social networking sites, cloud computing, websites and phishing. The various tools of forensics with the platform supported, the recent versions and licensing details are discussed. The paper extends with the current challenges being faced by digital forensics.


title: Advanced automated SQL injection attacks and defensive mechanisms

abstract: SQL Injection vulnerabilities still exist even after almost two decades that it first appeared. In spite of numerous prevention methodologies being used today, web applications still tend to be vulnerable to SQL injection attacks. Technology has improved drastically over the past few years and computers have certainly brought a great impact on our lifestyle. The computer applications and their usage over the web are myriad. It is quite evident that the in the near future, the usage of computers would relatively be higher than what we are witnessing today. A wide variety of data such as credit information, military data, human communication data, and countless types of data is shared over the far-flung computer networks. As the usage and reliability on computers increase, the threat to sensitive data likewise increases. The challenges with the cyber security when dealing with sensitive information is now a nightmare. To help understand the threats and the severity of exploits deployed, the paper provides proof of concepts for exploits carried out to compromise web applications and how the databases are exploited using the SQL injection methodologies. The SQL injection vulnerabilities in the web applications are surprisingly very vast and this is definitely is a huge security threat to personal data of people that is stored on web. In this paper, the methods used in information gathering, how the security is breached, and how payloads are used to exploit web applications are explained using the Kali Linux. In addition, an analysis is carried out on how the websites are comprised. Advanced methods on how to defend SQL injections are briefly justified. For the readers to understand better, a real time scenario of a penetration tester and a database server is set up with a few suppositions, and the commands that dodge the security characteristics and manipulate the databases are explicated.


title: Kanban in software development: A systematic literature review

abstract: Using of Kanban in software development is an emerging topic. This systematic literature review was conducted in order to analyze the current trend of Kanban usage in software development and to identify the obtained benefits and involved challenges. The search strategy resulted in 492 papers, of which 19 were identified as primary studies relevant to our research. The main reported benefits of using the Kanban method were improved lead time to deliver software, improved quality of software, improved communication and coordination, increased consistency of delivery, and decreased customer reported defects. The reported challenges included lack of knowledge and specialized training as well as various organizational issues. Additionally, suggested practices were extracted from the primary studies and summarized for guiding the practitioners interested in adopting Kanban. The findings of this literature review are intended for helping researchers and practitioners to gain a better understanding of the current state of Kanban usage in software development.


title: Performance analysis of allwinner A10 ARM processor based MeLE A1000 Mini PC with Linux OS

abstract: At the beginning of 2012 Shenzhen Mele Digital Technology Ltd. released the MeLE A1000 Mini PC, which is a low power Android device with media playing and browsing capabilities. The device is Linux friendly - its chipset supported by many distributions - so everybody can use it as a general purpose computer. In this paper this ARM based device is analyzed and their performance is compared with traditional architectures to explore the capabilities and advantages of this device with Linux operating system.


End-To-End Audio Replay Attack Detection Using Deep Convolutional Networks with Attention


title: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

abstract: A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.


title: Atlas-based rib-bone detection in chest X-rays

abstract: Abstract This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91 ± 1.8% for Montgomery and 85.48 ± 3.3% for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.


title: Adversarial Reinforcement Learning in a Cyber Security Simulation

abstract: This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo learning with the Softmax exploration strategy is most effective in performing the defender role and also for learning attacking strategies.


title: Like prediction: modeling like counts by bridging facebook pages with linked data

abstract: Recent growth of social media has produced a new market for branding of people and businesses. Facebook provides Facebook Pages (Pages in short) for public figures and businesses (we call entities) to communicate with their fans through a Like button. Because Like counts sometimes reflect the popularity of entities, techniques to increase the Like count can be a matter of interest, and might be known as social media marketing. From an academic perspective, Like counts of Pages depend not only on the popularity of the entity, but also on the popularity of semantically related entities. For example, Lady Gaga's Page has many Likes; her song "Poker Face" does too. We can infer that her next song will acquire many Likes immediately. Important questions are these: How does the Like count of Lady Gaga affect the Like count of her song? Alternatively, how does the Like count of her song constitute some fraction of the Like count of Lady Gaga herself? As described in this paper, we strive to reveal the mutual influences of Like counts among semantically related entities. To measure the influence of related entities, we propose a problem called the Like prediction problem (LPP). It models Like counts of a given entity using information of related entities. The semantic relations among entities, expressed as RDF predicates, are obtained by linking each Page with the most similar DBpedia entity. Using the model learned by support vector regression (SVR) on LPP, we can estimate the Like count of a new entity e.g., Lady Gaga's new song. More importantly, we can analyze which RDF predicates are important to infer Like counts, providing a mutual influence network among entities. Our study comprises three parts: (1) crawling the Pages and their Like counts, (2) linking Pages to DBpedia, and (3) constructing features to solve the LPP. Our study, based on 20 million Pages with 30 billion Likes, is the largest-scale study of Facebook Likes ever reported. This research constitutes a new attempt to integrate unstructured emotional data such as Likes, with Linked data, and to provide new insights for branding with social media.


title: Naive Bayes Classifier based Arabic document categorization

abstract: Text Categorization aims to assign an electronic document to one or more categories based on its contents. Due to the rapid growth of the number of online Arabic documents, the information libraries and Arabic document corpus, automatic Arabic document classification becomes an important task. This paper suggests the use of rooting algorithm with Naive Bayes Classifier to the problem of document categorization of Arabic language and reports the algorithm performance in terms of error rate, accuracy, and micro-average recall measures. Our experimental study shows that using rooting algorithm with Naive Bayes (NB) Classifier gives ~62.23% average accuracy and decreases the dimensionality of the training documents.


Comparison of Amplitude of Low Frequency Fluctuation between Schizophrenia and Bipolar Disorder Patients: a Resting-state Functional Magnetic Resonance Imaging Study


title: Bracing in adult with scoliosis: experience in diagnosis and classification from a 15 year prospective study of 739 patients

abstract: Background ::: Despite the frequency of adult scoliosis, very few publications concern the conservative orthopaedic treatments. The indications have not been defined to date. The experience of a department specialized in rigid bracing allows us to consolidate and clarify diagnosis and indications as well.


title: Dexterous Robotic Manipulation of Deformable Objects with Multi-Sensory Feedback - a Review

abstract: Designing autonomous robotic systems able to manipulate deformable objects without human intervention constitutes a challenging area of research. The complexity of interactions between a robot manipulator and a deformable object originates from a wide range of deformation characteristics that have an impact on varying degrees of freedom. Such sophisticated interaction can only take place with the assistance of intelligent multisensory systems that combine vision data with force and tactile measurements. Hence, several issues must be considered at the robotic and sensory levels to develop genuine dexterous robotic manipulators for deformable objects. This chapter presents a thorough examination of the modern concepts developed by the robotic community related to deformable objects grasping and manipulation. Since the convention widely adopted in the literature is often to extend algorithms originally proposed for rigid objects, a comprehensive coverage on the new trends on rigid objects manipulation is initially proposed. State-of-the-art techniques on robotic interaction with deformable objects are then examined and discussed. The chapter proposes a critical evaluation of the manipulation algorithms, the instrumentation systems adopted and the examination of end-effector technologies, including dexterous robotic hands. The motivation for this review is to provide an extensive appreciation of state-of-the-art solutions to help researchers and developers determine the best possible options when designing autonomous robotic systems to interact with deformable objects. Typically in a robotic setup, when robot manipulators are programmed to perform their tasks, they must have a complete knowledge about the exact structure of the manipulated object (shape, surface texture, rigidity) and about its location in the environment (pose). For some of these tasks, the manipulator becomes in contact with the object. Hence, interaction forces and moments are developed and consequently these interaction forces and moments, as well as the position of the end-effector, must be controlled, which leads to the concept of “force controlled manipulation” (Natale, 2003). There are different control strategies used in 28


Scale Space and Variational Methods in Computer Vision


title: Question identification on twitter

abstract: In this paper, we investigate the novel problem of automatic question identification in the microblog environment. It contains two steps: detecting tweets that contain questions (we call them "interrogative tweets") and extracting the tweets which really seek information or ask for help (so called "qweets") from interrogative tweets. To detect interrogative tweets, both traditional rule-based approach and state-of-the-art learning-based method are employed. To extract qweets, context features like short urls and Tweet-specific features like Retweets are elaborately selected for classification. We conduct an empirical study with sampled one hour's English tweets and report our experimental results for question identification on Twitter.


Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection


title: Commonly Uncommon: Semantic Sparsity in Situation Recognition

abstract: Semantic sparsity is a common challenge in structured visual classification problems, when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in situation recognition, the task of producing structured summaries of what is happening in images, including activities, objects and the roles objects play within the activity. For this problem, we find empirically that most substructures required for prediction are rare, and current state-of-the-art model performance dramatically decreases if even one such rare substructure exists in the target output. We avoid many such errors by (1) introducing a novel tensor composition function that learns to share examples across substructures more effectively and (2) semantically augmenting our training data with automatically gathered examples of rarely observed outputs using web data. When integrated within a complete CRF-based structured prediction model, the tensor-based approach outperforms existing state of the art by a relative improvement of 2.11% and 4.40% on top-5 verb and noun-role accuracy, respectively. Adding 5 million images with our semantic augmentation techniques gives further relative improvements of 6.23% and 9.57% on top-5 verb and noun-role accuracy.


title: Rearing Methods for the Black Soldier Fly (Diptera: Stratiomyidae)

abstract: The black soldier fly, Heretia illucens (L.), is a nonpest tropical and warm-temperate region insect that is useful for managing large concentrations of animal manure and other biosolids. Manure management relying on wild fly oviposition has been successful in several studies. However, confidence in this robust natural system was low and biological studies were hampered by the lack of a dependable source of eggs and larvae. Larvae had been reared easily by earlier investigators, but achieving mating had been problematic. We achieved mating reliably in a 2 by 2 by 4-m screen cage in a 7 by 9 by 5-m greenhouse where sunlight and adequate space for aerial mating were available. Mating occurred during the shortest days of winter if the sun was not obscured by clouds. Adults were provided with water, but no food was required. Techniques for egg collection and larval rearing are given. Larvae were fed a moist mixture of wheat bran, corn meal, and alfalfa meal. This culture has been maintained for 3 yr. Maintainance of a black soldier fly laboratory colony will allow for development of manure management systems in fully enclosed animal housing and in colder regions.


title: Immersive Virtual Reality for Reducing

abstract: This study explored the novel use of immersive virtual environments as a nonpharmacologic pain control technique and whether it works for both men and women. Fourteen female and 8 male students underwent pain induced via a blood pressure cuff ischemia lasting 10 min or less. Pain ratings increased significantly every 2 min during the no distraction phase (0 to 8 min) and dropped dramatically during the last 2 min period when participants were in the virtual environment (a 59% drop for women and a 41% drop for men). Five visual analog pain scores for each treatment condition served as the primary dependent variables. All 22 participants reported a drop in pain in the virtual environment, and the magnitude of pain reduction from the virtual environment was large (a 52% drop) and statistically significant. This is the first study to show immersive virtual environment distraction is also effective for women. The results show that virtual environments can function as a strong nonpharmacologic pain reduction te...


title: An O(v|v| c |E|) algoithm for finding maximum matching in general graphs

abstract: In this paper we present an 0(√|V|¿|E|) algorithm for finding a maximum matching in general graphs. This algorithm works in 'phases'. In each phase a maximal set of disjoint minimum length augmenting paths is found, and the existing matching is increased along these paths. Our contribution consists in devising a special way of handling blossoms, which enables an O(|E|) implementation of a phase. In each phase, the algorithm grows Breadth First Search trees at all unmatched vertices. When it detects the presence of a blossom, it does not 'shrink' the blossom immediately. Instead, it delays the shrinking in such a way that the first augmenting path found is of minimum length. Furthermore, it achieves the effect of shrinking a blossom by a special labeling procedure which enables it to find an augmenting path through a blossom quickly.


title: Low-Complexity Real-Time LDPC Encoder Design for CMMB

abstract: Based on modified LU decomposition theory with pivoting of sparse matrix, a low-complexity real-time LDPC encoder for CMMB was presented, which can support 2 different code rate (1/2 and 3/4). Multi- staged pipeline and Pingpong buffer architectures were used to improve throughput. An efficient memory organization for storing sparse matrices was also presented. Whole design was synthesized and routed on Altera Stratix II EP2S90. Highest frequency achieved over 200 MHz, with pure encoding rate 32.44 Mbps (1/2 code rate) and 67.16 Mbps (3/4 code rate). Beside, fully-parameterized LDPC encoder can support arbitrary H matrix LDPC code with only necessary initialized data of memory blocks.


title: Active Capacitive-Sensing Circuit Technique for Image Quality Improvement on Fingerprint Sensor

abstract: This paper proposes an active capacitive sensing circuit for fingerprint sensors, which includes a pixel level charge-sharing and charge pump to replace an ADC. This paper also proposes the operating algorithm for 16-level gray scale image. The active capacitive technology is more flexible and can be adjusted to adapt to a wide range of different skin types and environments. The proposed novel circuit is composed with unit gain buffer, 6-stage charge pump and analog comparator. The proper operation is validated by the HSPICE simulation of one pixel with condition of 0.35μm typical CMOS parameter and 3.3V power.


Nursing care of the child with cancer.


title: Cryptanalysis of Chosen Symmetric Homomorphic Schemes

abstract: Since Gentry’s breakthrough result was introduced in the year 2009, the homomorphic encryption has become a very popular topic. The main contribution of Gentry’s thesis was, that it has proven, that it actually is possible to design a fully homomorphic encryption scheme. However ground-breaking Gentry’s result was, the designs, that employ the bootstrapping technique suffer from terrible performance both in key generation and homomorphic evaluation of circuits. Some authors tried to design schemes, that could evaluate homomorphic circuits of arbitrarily many inputs without need of bootstrapping. This paper introduces notion of symmetric homomorphic encryption, analyses the security of four such proposals, published in three different papers. Our result is a known plaintext key-recovery attack on every one of these schemes.


title: Thinking forensics: cognitive science for forensic practitioners

abstract: Human factors and their implications for forensic science have attracted increasing levels of interest across criminal justice communities in recent years. Initial interest centred on cognitive biases, but has since expanded such that knowledge from psychology and cognitive science is slowly infiltrating forensic practices more broadly. This article highlights a series of important findings and insights of relevance to forensic practitioners. These include research on human perception, memory, context information, expertise, decision-making, communication, experience, verification, confidence, and feedback. The aim of this article is to sensitise forensic practitioners (and lawyers and judges) to a range of potentially significant issues, and encourage them to engage with research in these domains so that they may adapt procedures to improve performance, mitigate risks and reduce errors. Doing so will reduce the divide between forensic practitioners and research scientists as well as improve the value and utility of forensic science evidence.


Epigenetic assays for chemical biology and drug discovery


title: A Wideband L-Probes Fed Circularly-Polarized Reconfigurable Microstrip Patch Antenna

abstract: A wideband circularly polarized reconfigurable microstrip patch antenna fed by L-shaped probes is presented. Right hand circular polarization and left hand circular polarization could be excited by L-shaped probes feeding a perturbed square patch. The L-shaped probes are connected to a switch which is fabricated underneath the ground plane, such that circularly polarized radiation pattern reconfiguration could be realized. An antenna prototype was fabricated and it attains a bandwidth of over 10% with both SWR < 2 and axial ratio < 3 dB.


title: Second Language Acquisition: Reconciling Theories

abstract: This article argues that previous attempts to explain SLA ::: should not be disregarded. Instead, when they are put together, they provide a broader ::: and deeper view of the acquisition process. There is evidence to support the claim that second language acquisition ::: (SLA) is a complex adaptive system due to its inherent ability to adapt to different ::: conditions present in both internal and external environments. Based on this understanding, ::: widely discussed second language theories, including behaviorism, will be treated as ::: explanations of parts of a whole, since each captures a different aspect of SLA. In order to justify this assumption, excerpts ::: from some English language learning histories are provided to exemplify how ::: learners describe their learning processes. The final claim is that SLA should be seen as a chaotic/complex ::: system.


title: Deep Cross-Modal Projection Learning for Image-Text Matching

abstract: The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross-modal embeddings with the bi-directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remains a challenge in real applications. In this paper, we propose a cross-modal projection matching (CMPM) loss and a cross-modal projection classification (CMPC) loss for learning discriminative image-text embeddings. The CMPM loss minimizes the KL divergence between the projection compatibility distributions and the normalized matching distributions defined with all the positive and negative samples in a mini-batch. The CMPC loss attempts to categorize the vector projection of representations from one modality onto another with the improved norm-softmax loss, for further enhancing the feature compactness of each class. Extensive analysis and experiments on multiple datasets demonstrate the superiority of the proposed approach.


title: Adoption factors of NFC Mobile Proximity Payments in Italy

abstract: Mobile payment represents a promising emerging market. Nevertheless, especially for Mobile Proximity Payment (MPP), neither users nor merchants have largely adopted this innovation so far. This study aims to identify the adoption factors of MPP by developing a user model, tested through an in-lab experimentation involving 50 users in Italy. We then compared our results with those obtained through a remote survey that involved 1001 subjects who have never used a MPP system before. Compatibility with users' needs, habits and lifestyle has been found to be the dominating factor for adoption. Surprisingly, we found that a previous use of e-payment systems does not influence the user's perception of compatibility. While perceived security is a concern for prospective users who have never used MPP, it does not affect the intention to adopt for users who tried the system at least once. Cost considerations do not influence MPP adoption intention. Based on these findings we expect that MPP systems have a high chance to be widely adopted if optimized for compatibility.


title: Hierarchical Neural Story Generation

abstract: We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.


title: Risk Prediction on Electronic Health Records with Prior Medical Knowledge

abstract: Predicting the risk of potential diseases from Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Compared with traditional machine learning models, deep learning based approaches achieve superior performance on risk prediction task. However, none of existing work explicitly takes prior medical knowledge (such as the relationships between diseases and corresponding risk factors) into account. In medical domain, knowledge is usually represented by discrete and arbitrary rules. Thus, how to integrate such medical rules into existing risk prediction models to improve the performance is a challenge. To tackle this challenge, we propose a novel and general framework called PRIME for risk prediction task, which can successfully incorporate discrete prior medical knowledge into all of the state-of-the-art predictive models using posterior regularization technique. Different from traditional posterior regularization, we do not need to manually set a bound for each piece of prior medical knowledge when modeling desired distribution of the target disease on patients. Moreover, the proposed PRIME can automatically learn the importance of different prior knowledge with a log-linear model.Experimental results on three real medical datasets demonstrate the effectiveness of the proposed framework for the task of risk prediction


title: A game-theoretic approach to coalition formation in fog provider federations

abstract: In this paper we deal with the problem of making a set of Fog Infrastructure Providers (FIPs) increase their profits when allocating their resources to process the data generated by IoT applications that need to meet specific QoS targets in face of time-varying workloads. We show that if FIPs cooperate among them, by mutually sharing their workloads and resources, then each one of them can improve its net profit. By using a game-theoretic framework, we study the problem of forming stable coalitions among FIPs. Furthermore, we propose a mathematical optimization model for profit maximization to allocate IoT applications to a set of FIPs, in order to reduce costs and, at the same time, to meet the corresponding QoS targets. Based on this, we propose an algorithm, based on cooperative game theory, that enables each FIP to decide with whom to cooperate in order to increase its profits. The effectiveness of the proposed algorithm is demonstrated through an experimental evaluation considering various workload intensities. The results we obtain from these experiments show the ability of our algorithm to form coalitions of FIPs that are stable and profitable in all the scenarios we consider.


title: Privately Outsourcing Exponentiation to a Single Server: Cryptanalysis and Optimal Constructions

abstract: We address the problem of speeding up group computations in cryptography using a single untrusted computational resource. We analyze the security of an efficient protocol for securely outsourcing multi-exponentiations proposed at ESORICS 2014. We show that this scheme does not achieve the claimed security guarantees and we present practical polynomial-time attacks on the delegation protocol which allow the untrusted helper to recover part (or the whole) of the device secret inputs. We then provide simple constructions for outsourcing group exponentiations in different settings (e.g. public/secret, fixed/variable bases and public/secret exponents). Finally, we prove that our attacks on the ESORICS 2014 protocol are unavoidable if one wants to use a single untrusted computational resource and to limit the computational cost of the limited device to a constant number of (generic) group operations. In particular, we show that our constructions are actually optimal in terms of operations in the underlying group.


title: Sudden Deaths: Taking Stock of Geographic Ties

abstract: Analysis of a worldwide sample of sudden deaths of politicians reveals a market-adjusted 1.7% decline in the value of companies headquartered in the politician's hometown. The decline in value is followed by a drop in the rate of growth in sales and access to credit. Our results are particularly pronounced for family firms, firms with high growth prospects, firms in industries over which the politician has jurisdiction, and firms headquartered in highly corrupt countries.


title: Exploiting Subjectivity Classification to Improve Information Extraction

abstract: Information extraction (IE) systems are prone to false hits for a variety of reasons and we observed that many of these false hi ts occur in sentences that contain subjective language (e.g., opinions, emotions, and sentiments). Motivated by these observations, we explore the idea of using subjectivity analysis to improve the precision of information extraction systems. In this paper, we describe an IE system that uses a subjective sentence classifier to filter its extractions. We experimented with several different strategies for using the subjectivity classifications, including an aggressive strategy that discards all extractions found in subjective sentences and more complex strategies that selectively discard extractions. We evaluated the performance of these different approaches on the MUC-4 terrorism data set. We found that indiscriminately filtering extractions from subjective sentences was overly aggressive, but more selective filtering strategies improved IE precision with minimal recall loss.


title: Measuring Fluid Intelligence in Healthy Older Adults

abstract: The present study evaluated subjective and objective cognitive measures as predictors of fluid intelligence in healthy older adults. We hypothesized that objective cognitive measures would predict fluid intelligence to a greater degree than self-reported cognitive functioning. Ninety-three healthy older (>65 years old) community-dwelling adults participated. Raven's Advanced Progressive Matrices (RAPM) were used to measure fluid intelligence, Digit Span Sequencing (DSS) was used to measure working memory, Trail Making Test (TMT) was used to measure cognitive flexibility, Design Fluency Test (DFT) was used to measure creativity, and Tower Test (TT) was used to measure planning. The Cognitive Failures Questionnaire (CFQ) was used to measure subjective perceptions of cognitive functioning. RAPM was correlated with DSS, TT, and DFT. When CFQ was the only predictor, the regression model predicting fluid intelligence was not significant. When DSS, TMT, DFT, and TT were included in the model, there was a significant change in the model and the final model was also significant, with DFT as the only significant predictor. The model accounted for approximately 20% of the variability in fluid intelligence. Our findings suggest that the most reliable means of assessing fluid intelligence is to assess it directly.


title: Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems

abstract: Energy-harvesting technology provides a promising platform for future IoT applications. However, since communication is very expensive in these devices, applications will require inference "beyond the edge" to avoid wasting precious energy on pointless communication. We show that application performance is highly sensitive to inference accuracy. Unfortunately, accurate inference requires large amounts of computation and memory, and energy-harvesting systems are severely resource-constrained. Moreover, energy-harvesting systems operate intermittently, suffering frequent power failures that corrupt results and impede forward progress. This paper overcomes these challenges to present the first full-scale demonstration of DNN inference on an energy-harvesting system. We design and implement SONIC, an intermittence-aware software system with specialized support for DNN inference. SONIC introduces loop continuation, a new technique that dramatically reduces the cost of guaranteeing correct intermittent execution for loop-heavy code like DNN inference. To build a complete system, we further present GENESIS, a tool that automatically compresses networks to optimally balance inference accuracy and energy, and TAILS, which exploits SIMD hardware available in some microcontrollers to improve energy efficiency. Both SONIC & TAILS guarantee correct intermittent execution without any hand-tuning or performance loss across different power systems. Across three neural networks on a commercially available microcontroller, SONIC & TAILS reduce inference energy by 6.9× and 12.2×, respectively, over the state-of-the-art.


title: Learning with Latent Language

abstract: The named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter's loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without.


title: CEO/CIO mutual understanding, strategic alignment, and the contribution of IS to the organization

abstract: Mutual understanding between the CEO and CIO is thought to facilitate the alignment of an organization's IS with its business strategy, and thereby enhance the contribution of the IS to business performance. A survey of 202 pairs of CEOs and CIOs was taken to investigate the relationships between them. Mutual understanding was measured as the role of IS in the organization, using the perspectives of both executives. Strategic alignment was measured as the fit between the CEO's assessment of eight STROBE dimensions and the CIO's assessment of the analogous STROEPIS dimensions; both the CEO and CIO evaluated IS contribution. Mutual understanding of the role of IT led to seven alignment dimensions whereas six alignment dimensions led to IS contributions. Our study extended the theory of IT strategic alignment and provided direction for CEOs and CIOs interested in improving the IS contribution of their organization.


title: Double LDA: A Sentiment Analysis Model Based on Topic Model

abstract: The sentiment mining is a fast growing topic of both academic research and commercial applications, especially with the widespread of short-text applications on the Web. A fundamental problem that confronts sentiment mining is the automatics and correctness of mined sentiment. This paper proposes an DLDA (Double Latent Dirichlet Allocation) model to analyze sentiment for short-texts based on topic model. Central to DLDA is to add sentiment to topic model and consider sentiment as equal to topic, but independent of topic. DLDA is actually two methods DLDA I and its improvement DLDA II. Compared to the single topic-word LDA, the double LDA I, i.e., DLDA I designs another sentiment-word LDA. Both LDAs are independent of each other, but they combine to influence the selected words in short-texts. DLDA II is an improvement of DLDA I. It employs entropy formula to assign weights of words in the Gibbs sampling based on the ideas that words with stronger sentiment orientation should be assigned with higher weights. Experiments show that compared with other traditional topic methods, both DLDA I and II can achieve higher accuracy with less manual needs.


title: BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications

abstract: An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.


title: Diagnosis of pathology on the vertebral column with backpropagation and Naive Bayes classifier

abstract: Minimizing human errors in diagnosis is one of the most important aims of Computer-Aided Diagnosis (CAD) systems. CAD software is the software helping radiologists , specialist doctor for detect abnormality on medical views by using advanced pattern diagnosis and view processing methods. In this study, a CAD system that can help detection and diagnosis of Pathology on the Vertebral Column is introduced. The vertebral database used in the application is obtained from UCI (Machine Learning Repository). Various artificial intelligence methods were applied for the classification of this database obtained. The results obtained are comparatively explained in detail in the related part.


title: An Assessment of Case Base Reasoning for Short Text Message Classification

abstract: Message classification is a text classification task that has provoked much interest in machine learning. One aspect of message classification that presents a particular challenge is the classification of short text messages. This paper presents an assessment of applying a casebased reasoning approach that was developed for long text messages (specifically spam filtering) to short text messages. The evaluation involves determining the most appropriate feature types and feature representation for short text messages and then comparing the performance of the case-based classifier with both a Naive Bayes classifier and a Support Vector Machine. Our evaluation shows that short text messages require different features and even different classifiers than long text messages. A machine learner which is to classify text messages will require some level of configuration in these aspects.


title: Generative Choreography using Deep Learning

abstract: Recent advances in deep learning have enabled the extraction of high-level features from raw sensor data which has opened up new possibilities in many different fields, including computer generated choreography. In this paper we present a system chor-rnn for generating novel choreographic material in the nuanced choreographic language and style of an individual choreographer. It also shows promising results in producing a higher level compositional cohesion, rather than just generating sequences of movement. At the core of chor-rnn is a deep recurrent neural network trained on raw motion capture data and that can generate new dance sequences for a solo dancer. Chor-rnn can be used for collaborative human-machine choreography or as a creative catalyst, serving as inspiration for a choreographer.


title: Scene Text Detection and Segmentation Based on Cascaded Convolution Neural Networks

abstract: Scene text detection and segmentation are two important and challenging research problems in the field of computer vision. This paper proposes a novel method for scene text detection and segmentation based on cascaded convolution neural networks (CNNs). In this method, a CNN-based text-aware candidate text region (CTR) extraction model (named detection network, DNet) is designed and trained using both the edges and the whole regions of text, with which coarse CTRs are detected. A CNN-based CTR refinement model (named segmentation network, SNet) is then constructed to precisely segment the coarse CTRs into text to get the refined CTRs. With DNet and SNet, much fewer CTRs are extracted than with traditional approaches while more true text regions are kept. The refined CTRs are finally classified using a CNN-based CTR classification model (named classification network, CNet) to get the final text regions. All of these CNN-based models are modified from VGGNet-16. Extensive experiments on three benchmark data sets demonstrate that the proposed method achieves the state-of-the-art performance and greatly outperforms other scene text detection and segmentation approaches.


title: Toward Smarter Vulnerability Discovery Using Machine Learning

abstract: A Cyber Reasoning System (CRS) is designed to automatically find and exploit software vulnerabilities in complex software. To be effective, CRSs integrate multiple vulnerability detection tools (VDTs), such as symbolic executors and fuzzers. Determining which VDTs can best find bugs in a large set of target programs, and how to optimally configure those VDTs, remains an open and challenging problem. Current solutions are based on heuristics created by security analysts that rely on experience, intuition and luck. In this paper, we present Central Exploit Organizer (CEO), a proof-of-concept tool to optimize VDT selection. CEO uses machine learning to optimize the selection and configuration of the most suitable vulnerability detection tool. We show that CEO can predict the relative effectiveness of a given vulnerability detection tool, configuration, and initial input. The estimation accuracy presents an improvement between $11%$ and $21%$ over random selection. We are releasing CEO and our dataset as open source to encourage further research.


title: Inhomogeneous Bulk Viscous Fluid Universe with Electromagnetic Field and Variable $\Lambda$-Term

abstract: Cylindrically symmetric inhomogeneous cosmological model for bulk viscous fluid distribution with electromagnetic field is obtained. The source of the magnetic field is due to an electric current produced along the z-axis. F12 is the non-vanishing component of electromagnetic field tensor. To get the deterministic solution, it has been assumed that the expansion θ in the model is proportional to the shear σ. The values of cosmological constant for these models are found to be small and positive at late time, which are consistent with the results from recent supernovae Ia observations. Physical and geometric aspects of the models are also discussed in presence and absence of magnetic field.


title: Infrastructure-Free Content-Based Publish/Subscribe

abstract: Peer-to-peer (P2P) networks can offer benefits to distributed content-based publish/subscribe data dissemination systems. In particular, since a P2P network's aggregate resources grow as the number of participants increases, scalability can be achieved using no infrastructure other than the participants' own resources. This paper proposes algorithms for supporting content-based publish/subscribe in which subscriptions can specify a range of interest and publications a range of values. The algorithms are built over a distributed hash table abstraction and are completely decentralized. Load balance is addressed by subscription delegation away from overloaded peers and a bottom-up tree search technique that avoids root hotspots. Furthermore, fault tolerance is achieved with a lightweight replication scheme that quickly detects and recovers from faults. Experimental results support the scalability and fault-tolerance properties of the algorithms: For example, doubling the number of subscriptions does not double internal system messages, and even the simultaneous failure of 20% of the peers in the system requires less than 2 min to fully recover.


title: Psychological Barriers: Lurker and Poster Motivation and Behavior in Online Communities

abstract: Virtual communities provide an attractive place for organizations to mine information regarding customer perceptions, needs, and demographics; as well as to generate revenue through sales of products, services, information, and advertising. However, the community conversation provides information about only one type of community user, the poster. Information about the lurker, who never posts, is conspicuously absent from the obvious community data source, the postings. Lurkers may be a large portion of the user community and could provide key revenue sources and vital information, or they potentially could turn into posters. This research contrasts the differences in the underlying motivations of lurkers, infrequent posters, and posters in order to understand the resulting differences in their behavior. 518 users from 20 virtual communities were categorized into three groups based upon their posting behaviors: lurkers who never posted, infrequent posters who posted three or less times per month, and frequent posters. Results revealed that lurkers differed significantly from posters, especially in their willingness to give information and exchange social support. There was a gradual progression from lurker to poster regarding the desires to get knowledge and obtain shopping information. Implications about a possible psychological barrier regarding giving information and social support are discussed.


title: Fully collusion secure dynamic broadcast encryption with constant-size ciphertexts or decryption keys

abstract: This paper puts forward new efficient constructions for public-key broadcast encryption that simultaneously enjoy the following properties: receivers are stateless; encryption is collusion-secure for arbitrarily large collusions of users and security is tight in the standard model; new users can join dynamically i.e. without modification of user decryption keys nor ciphertext size and little or no alteration of the encryption key. We also show how to permanently revoke any subgroup of users. Most importantly, our constructions achieve the optimal bound of O(1)-size either for ciphertexts or decryption keys, where the hidden constant relates to a couple of elements of a pairing-friendly group. Our broadcast-KEM trapdoor technique, which has independent interest, also provides a dynamic broadcast encryption system improving all previous efficiency measures (for both execution time and sizes) in the private-key setting.


title: Television, Video Game and Social Media Use Among Children with ASD and Typically Developing Siblings

abstract: This study examined the nature of television, video game, and social media use in children (ages 8–18) with autism spectrum disorders (ASD, n = 202) compared to typically developing siblings (TD, n = 179), and relative to other activities. Parents completed measures assessing children’s screen-based and other extracurricular activities. Children with ASD spent approximately 62 % more time watching television and playing video games than in all non-screen activities combined. Compared with TD siblings, children with ASD spent more hours per day playing video games (2.4 vs. 1.6 for boys, and 1.8 vs. 0.8 for girls), and had higher levels of problematic video game use. In contrast, children with ASD spent little time using social media or socially interactive video games.


title: Cytotoxic chaetoglobosins from the endophyte Chaetomium globosum

abstract: Two new alkaloids chaetoglobosins V ( 1) and W ( 2), together with the six known congeners 3- 8, were isolated through bioassay-guided fractionations from the EtOAc extract of a solid culture of CHAETOMIUM GLOBOSUM IFB-E041. The structures were elucidated by spectroscopic methods including mainly 1D and 2D NMR techniques. Chaetoglobosin W ( 2) was unique in its possession of an oxolane ring formed via an oxygen bridge between C-3 and C-6. The isolated fungal metabolites exhibited moderate cytotoxic activities against four human cancer cell lines (KB, K562, MCF-7, and HepG2) with their IC 50 values in a range of 18-30 μg/mL.


title: Predictive Performance Comparison Analysis of Relational & NoSQL Graph Databases

abstract: From last three decades, the relational databases ::: are being used in many organizations of various natures such ::: as Education, Health, Business and in many other applications. ::: Traditional databases show tremendous performance and are ::: designed to handle structured data with ACID (Atomicity, Consistency, ::: Isolation, Durability) property to manage data integrity. ::: In the current era, organizations are storing more data i.e. videos, ::: images, blogs, etc. besides structured data for decision making. ::: Similarly, social media and scientific applications are generating ::: large amount of semi-structured data of varied nature. Relational ::: databases cannot process properly and manage such large amount ::: of data efficiently. To overcome this problem, another paradigm ::: NoSQL databases is introduced to manage and process massive ::: amount of unstructured data efficiently. NoSQL databases are ::: divided into four categories and each category is used according ::: to the nature and need of the specific problem. In this paper ::: we will compare Oracle relational database and NoSQL graph ::: database using optimized queries and physical database tuning ::: techniques. The comparison is two folded: in the first iteration we ::: compare various kinds of queries such as simpler query, database ::: tuning of Oracle relational database such as sub databases and ::: perform these queries in our desired environments. Secondly, for ::: this comparison we will perform predictive analysis for the results ::: obtained from our experiments.


title: Flipping 419 Cybercrime Scams: Targeting the Weak and the Vulnerable

abstract: Most of cyberscam-related studies focus on threats perpetrated against the Western society, with a particular attention to the USA and Europe. Regrettably, no research has been done on scams targeting African countries, especially Nigeria, where the notorious and (in)famous 419 advanced-fee scam, targeted towards other countries, originated. How- ever, as we know, cybercrime is a global problem affecting all parties. In this study, we investigate a form of advance fee fraud scam unique to Nigeria and targeted at Nigerians, but unknown to the Western world. For the study, we rely substantially on almost two years worth of data harvested from an on-line discussion forum used by criminals. We complement this dataset with recent data from three other active forums to consolidate and generalize the research. We apply machine learning to the data to understand the criminals' modus operandi. We show that the criminals exploit the socio-political and economic problems prevalent in the country to craft various fraud schemes to defraud vulnerable groups such as secondary school students and unemployed graduates. The result of our research can help potential victims and policy makers to develop measures to counter the activities of these criminal groups.


title: Sensitive detection of interleukin-6 on a plasmonic chip by grating-coupled surface-plasmon-field-enhanced fluorescence imaging

abstract: A plasmonic chip, which is a grating substrate coated with metal films, was fabricated and applied to grating-coupled surface-plasmon-field-enhanced fluorescence imaging (GC-SPFI). With the enhanced fluorescence excited by the surface plasmon field on the plasmonic chip, interleukin-6 (IL-6), a marker protein of lifestyle-related diseases, was measured using a sandwich assay system. IL-6 was quantitatively determined to be up to 2 pg/mL by GC-SPFI. The detection sensitivity for IL-6 was superior to that with an enzyme-linked immunosorbent assay, and GC-SPFI was shown to be a valid method for immunosensing.


title: Rhinitis sicca, dry nose and atrophic rhinitis: a review of the literature

abstract: Despite the fact that many people suffer from it, an unequivocal definition of dry nose (DN) is not available. Symptoms range from the purely subjective sensation of a rather dry nose to visible crusting of the (inner) nose (nasal mucosa), and a wide range of combinations are met with. Relevant diseases are termed rhinitis sicca anterior, primary and secondary rhinitis atrophicans, rhinitis atrophicans with foetor (ozena), and empty nose syndrome. The diagnosis is based mainly on the patient's history, inspection of the external and inner nose, endoscopy of the nasal cavity (and paranasal sinuses) and the nasopharynx, with CT, allergy testing and microbiological swabs being performed where indicated. Treatment consists in the elimination of predisposing factors, moistening, removal of crusts, avoidance of injurious factors, care of the mucosa, treatment of infections and where applicable, correction of an over-large air space. Since the uncritical resection of the nasal turbinates is a significant and frequent factor in the genesis of dry nose, secondary RA and ENS, the inferior and middle turbinate should not be resected without adequate justification, and the simultaneous removal of both should not be done other than for a malignant condition. In this paper, we review both the aetiology and clinical presentation of the conditions associated with the symptom dry nose, and its conservative and surgical management.


title: A Two-Branch Network with Semi-Supervised Learning for Hyperspectral Classification

abstract: In order to promote progress on fusion and analysis methodologies for multi-source remote sensing data, The Image Analysis and Data Fusion Technical Committee organized the 2018 IEEE GRSS Data Fusion contest. In this contest, we proposed a two-branch convolution network for hyperspectral image classification with a data re-sampling strategy and semi-supervised learning to address three existing problems, i.e. multi-scale feature learning, data imbalance, and small size of the dataset. The contest showed that our proposal achieved the best performance on two metrics: the overall accuracy of 77.39% and a kappa coefficient of 0.76 on the hyperspectral images provided by 2018 IEEE GRSS Data Fusion Contest.


title: Real-time detection of rotated patterns using FPGA

abstract: In this paper, we describe an approach for real-time detection of rotated patterns in an image using FPGA. In many approaches, a two-dimensional pattern and the target regions in the image are transformed to one-dimensional arrays or several pattern specific values which are invariant to rotation, and the regions in which the pattern may exist are listed up using them. These approaches make it possible to narrow down the candidate regions with less computational cost, but the sensitivity depends on how to define the transform method. In our approach, each region in a given image is directly compared with a sequence of the rotated patterns using direct cross-correlation, and the positions and rotation angles of the patterns in the image can be found during a scan. This approach requires high computational cost, but by calculating the cross-correlations of a region and the sequence of the rotated patterns incrementally starting from the non-rotated pattern, and by reducing the number of the operations for the cross-correlation considering the equality of the pixel values in the pattern, it becomes possible to realize real-time detection of the rotated patterns with one FPGA.


title: Osteopathy for primary headache patients: a systematic review

abstract: OBJECTIVE ::: This systematic review aimed to assess the efficacy, effectiveness, safety, and tolerability of osteopathic manipulative treatment (OMT) in patients with headache. ::: ::: ::: BACKGROUND ::: Migraine is one of the most common and disabling medical conditions. It affects more than 15% of the general population, causing high global socioeconomic costs, and the currently available treatment options are inadequate. ::: ::: ::: METHODS ::: We systematically reviewed all available studies investigating the use of OMT in patients with migraine and other forms of headache. ::: ::: ::: RESULTS ::: The search of literature produced six studies, five of which were eligible for review. The reviewed papers collectively support the notion that patients with migraine can benefit from OMT. OMT could most likely reduce the number of episodes per month as well as drug use. None of the included studies, however, was classified as low risk of bias according to the Cochrane Collaboration's tool for assessing risk of bias. ::: ::: ::: CONCLUSION ::: The results from this systematic review show a preliminary low level of evidence that OMT is effective in the management of headache. However, studies with more rigorous designs and methodology are needed to strengthen this evidence. Moreover, this review suggests that new manual interventions for the treatment of acute migraine are available and developing.


title: Content based video retrieval based on bounded coordinate of motion histogram

abstract: In this paper, the authors present a novel Content-Based Video Retrieval (CBVR) system based on Bounded Coordinate of Motion Histogram (BCMH). The authors propose to characterize videos by using spatio-temporal features (e.g. motion direction, intensity and the residual information features). Bounded Coordinate of Motion Histogram is introduced to characterize and compare video subsequences in a reduced computation time. Moreover, the proposed approach is adaptive: a training procedure is presented. For instance, the cost — expressed in similarity measurement — has its precision increased by more than twice 38%) in comparison with extended fast dynamic time warping approach on a dataset of 1707 movie clips.


title: DCFNet: Deep Neural Network with Decomposed Convolutional Filters

abstract: Filters in a Convolutional Neural Network (CNN) contain model parameters learned from enormous amounts of data. In this paper, we suggest to decompose convolutional filters in CNN as a truncated expansion with pre-fixed bases, namely the Decomposed Convolutional Filters network (DCFNet), where the expansion coefficients remain learned from data. Such a structure not only reduces the number of trainable parameters and computation, but also imposes filter regularity by bases truncation. Through extensive experiments, we consistently observe that DCFNet maintains accuracy for image classification tasks with a significant reduction of model parameters, particularly with Fourier-Bessel (FB) bases, and even with random bases. Theoretically, we analyze the representation stability of DCFNet with respect to input variations, and prove representation stability under generic assumptions on the expansion coefficients. The analysis is consistent with the empirical observations.


title: Performance of Feedback and Switch Space Diversity 900 MHz FM Mobile Radio Systems with Rayleigh Fading

abstract: A theoretical and experimental comparison of performance has been made between two types of predetection switching space diversity mobile radio systems. This comparison was made at a frequency of 840 MHz using simulated Rayleigh fading for a vehicle speed of about 80 mi/h. The switch diversity system was a conventional receiver antenna switching technique with two simulated physically separated receiving antennas and a single transmitting antenna. The feedback diversity system used a single receiving antenna with two simulated physically separated transmitting antennas. The transmitting antennas were switched remotely from the receiver. The difference in the performance of the two systems was shown to be primarily due to time delay inherent in the remote antenna switching technique.


title: The Impact of Rapid Incident Detection on Freeway Accident Fatalities

abstract: Incident detection systems have the potential of reducing the time between the occurrence of accidents and the notification of emergency medical services, called the accident notification time. Reductions in this time, in turn, may affect the numbers of fatalities. A statistical analysis is conducted to determine the quantitative relationship between fatalities and the accident notification time on urban freeways. Using this relationship, the impact of freeway incident detection systems on fatalities is estimated. are also derived. The economic benefits of fatality reduction


title: Advantages , Challenges and Contradictions of the Transformative Nature of Telework : A Review of the Literature Completed Research Paper

abstract: Information and communication technologies (ICT) have had, and continue to have, a profound impact on the way work is organized, undertaken and managed by organizations and their employees. Telework, in particular, allows employees to work anywhere and anytime. These developments are not simply extending earlier temporal and spatial boundaries of work; they are profoundly changing the nature of work itself. To gain a better understanding of the changing nature of work and the implications for organizations and employees, we conducted a literature review that identified in total 24 different advantages and challenges of telework. We also show that there are inherent contradictions among these advantages and challenges, thus outlining interesting questions and opportunities for future research and alerting practitioners to possible dilemmas arising from telework arrangements.


title: Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

abstract: Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object's location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the lambda2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.


title: Functional rhinoplasty.

abstract: The complete rhinoplasty surgeon must possess an understanding of functional nasal airway obstruction. An increasingly sophisticated grasp of the pathophysiology of fixed nasal obstruction has led surgeons to develop and refine surgical techniques aimed toward alleviating nasal valve insufficiency. This article reviews an assortment of techniques within nasal valve surgery, highlighting the underlying pathophysiology, anatomy, and technical considerations.