title: A direct AC LED driver with high power factor without the use of passive components

abstract: In this paper, a novel driver for high brightness LED applications that can directly run from AC voltage is presented. Conventional LED drivers for such applications consist of switching-type converters which require passive components like high value inductors and electrolytic capacitors for their operation. The use of such components increases the size and cost of the LED system while decreasing the overall life time. The proposed circuit only needs active devices like MOSFETs and op-amps for power processing. The input current is selectively controlled to follow a sinusoidal waveform to achieve low harmonic distortion and high power factor. A simple start-up circuit is also designed for self-sufficient operation with a more efficient set-up under development. The efficiency of the proposed system is around 82% for a 1.5W converter with 9% total harmonic distortion. Such an implementation is also more suitable for IC design. Both simulation and experimental results have been presented to validate the operation of the proposed set-up.


title: Web Services: A Comparison of Soap and Rest Services

abstract: The interest on Web services has been growing rapidly in these couple of years since their start of use. A web service would be described as a method for exchanging/communicating information between devices over a network. Often, when deciding which service would fit on the architecture design to develop a product, then the question rises which service to use and when? SOAP (Simple Object Access Protocol) and REST (Representational State Transfer) are the two most used protocols to exchange messages, so choosing one over the other has its own advantages and disadvantages. In this paper we have addressed the differences and best practices when to use one over the other.


Stochastic Online Anomaly Analysis for Streaming Time Series


Reverse Engineering of SPARQL Queries using Examples Anonymous


title: A New Level Shifter with Low Power in Multi-Voltage System

abstract: A new level shifter used in multiple voltage digital circuits is presented. It combines the merit of conventional level shifter and single supply level shifter, which can shifter any voltage level signal to a desired higher level with low leakage current. The circuits was designed in 180nm CMOS technology and simulated in SPICE. The simulation results showed that the proposed level shifter circuit has 36% leakage power dissipation reduction compared to the conventional level shifter


title: A Robust Main-Memory Compression Scheme

abstract: Lossless data compression techniques can potentially free up more than 50% of the memory resources. However, previously proposed schemes suffer from high access costs. The proposed main-memory compression scheme practically eliminates performance losses of previous schemes by exploiting a simple and yet effective compression scheme, a highly-efficient structure for locating a compressed block in memory, and a hierarchical memory layout that allows compressibility of blocks to vary with a low fragmentation overhead. We have evaluated an embodiment of the proposed scheme in detail using 14 integer and floating point applications from the SPEC2000 suite along with two server applications and we show that the scheme robustly frees up 30% of the memory resources, on average, with a negligible impact on the performance of only 0.2% on average.


title: Development of magnetic adhesion based climbing robot for non-destructive testing

abstract: This paper investigates the effect of key design parameters involved in optimizing the adhesion force achieved from rare earth neodymium magnets. In order to generate high adhesion force by using minimum number of permanent magnets, criteria such as distance between multiple magnets, thickness of flux concentrator have been evaluated by implementing Finite Element Analysis (FEA). Results show that adhesion force increases with increased distance between magnets and by using thick magnetic flux concentrator. A prototype robot has been designed to validate the simulation results. Experiment vs simulation study presents similar results. The robot is controlled remotely via Bluetooth communication protocol and provides umbilical free access to the robot operator.


title: Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms

abstract: Ammonia oxidation, as the first step in the nitrification process, plays a central role in the global cycling of nitrogen. Although bacteria are traditionally considered to be responsible for ammonia oxidation, a role for archaea has been suggested by data from metagenomic studies and by the isolation of a marine, autotrophic, ammonia-oxidizing, non-thermophilic crenarchaeon. Evidence for ammonia oxidation by non-thermophilic crenarchaea in marine and terrestrial environments is largely based on abundance of bacterial and archaeal ammonia monooxygenase (amo) genes, rather than activity. In this study, we have determined the influence of temperature on the response of ammonia-oxidizing bacteria and archaea in nitrifying soil microcosms using two approaches, involving analysis of transcriptional activity of 16S rRNA genes and of a key functional gene, amoA, which encodes ammonia monooxygenase subunit A. There was little evidence of changes in relative abundance or transcriptional activity of ammonia-oxidizing bacteria during nitrification. In contrast, denaturing gradient gel electrophoresis analysis of crenarchaeal 16S rRNA and crenarchaeal amoA genes provided strong evidence of changes in community structure of active archaeal ammonia oxidizers. Community structure changes were similar during incubation at different temperatures and much of the activity was due to a group of non-thermophilic crenarchaea associated with subsurface and marine environments, rather than soil. The findings suggest a role for crenarchaea in soil nitrification and that further information is required on their biogeography.


title: Ridge body parts features for human pose estimation and recognition from RGB-D video data

abstract: This paper addresses the issues of 3D human pose estimation, tracking and recognition from RGB-D video sequences using a generative structured framework. Most existing approaches focus on these issues using discriminative models. However, a discriminative model has certain drawbacks: a) it requires expensive training steps and large amount of training samples for covering inherently wide pose space, and (b) not suitable for real-time applications due to its slow algorithmic inferences. In this work, a real-time tracking system has been proposed for human pose recognition utilizing ridge body parts features. Initially, depth silhouettes extract ridge data inside the binary edges and initialize each body joints information using predefined pose. Then, body parts tracking incorporates appearance learning to handle occlusions and manage body joints features. Lastly, Support Vector Machine is used to recognize different poses. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes.


title: Talking about code: Integrating pedagogical code reviews into early computing courses

abstract: Given the increasing importance of soft skills in the computing profession, there is good reason to provide students with more opportunities to learn and practice those skills in undergraduate computing courses. Toward that end, we have developed an active learning approach for computing education called the Pedagogical Code Review (PCR). Inspired by the code inspection process used in the software industry, a PCR is a collaborative activity in which a small team of students, led by a trained moderator: (a) walk through segments of each other's programming solutions, (b) check the code against a list of best coding practices, and (c) discuss and log issues that arise. To evaluate the viability and effectiveness of this approach, we conducted a series of four mixed-method empirical studies of various implementations of PCRs in CS1 courses at Washington State University. The first study validated the viability of the PCR activity. Using a quasi-experimental design, the final three studies evaluated two alternative implementations of PCRs—face-to-face and online. Our results provide evidence that PCRs can promote positive attitudinal shifts, and hone skills in critical review, teamwork, and communication. Based on our findings, we present a set of best practices for implementing PCRs.


title: ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI

abstract: Background: There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans. Objective: To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images. Methods: We obtained conventional PDw and T2w images from 10 patients with relapsing–remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML. Results: Our study’s ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively. Conclusion: ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.


title: MicroRNAs in cancer: biomarkers, functions and therapy.

abstract: The emergence of microRNAs has been one of the defining developments in cancer biology over the past decade, and the explosion of knowledge in this area has brought forward new diagnostic and therapeutic opportunities. The importance of microRNAs in cancer has been underlined by the identification of alterations in microRNA target binding sites and the microRNA processing machinery in tumor cells. Clinical trials utilizing microRNA profiling for patient prognosis and clinical response are now underway, and the first microRNA mimic entered the clinic for cancer therapy in 2013. In this article we review the potential applications of microRNAs for the clinical assessment of patient outcome in cancer, as well as in cancer monitoring and therapy.


Option Pricing Using Neural Networks


title: Memes online: Extracted, subtracted, injected, and recollected

abstract: Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media.


title: The analysis of feature selection methods and classification algorithms in permission based Android malware detection

abstract: Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software. In the present study, we analyze two major aspects of permission-based malware detection in Android applications: Feature selection methods and classification algorithms. Within the framework of the assumptions specified for the analysis and the data used for the analysis, our findings reveal a higher performance for the Random Forest and J48 decision tree classification algorithms for most of the selected feature selection methods.


title: Defeating DDoS attacks by fixing the incentive chain

abstract: Cooperative technological solutions for Distributed Denial-of-Service (DDoS) attacks are already available, yet organizations in the best position to implement them lack incentive to do so, and the victims of DDoS attacks cannot find effective methods to motivate them. In this article we discuss two components of the technological solutions to DDoS attacks: cooperative filtering and cooperative traffic smoothing by caching. We then analyze the broken incentive chain in each of these technological solutions. As a remedy, we propose usage-based pricing and Capacity Provision Networks, which enable victims to disseminate enough incentive along attack paths to stimulate cooperation against DDoS attacks.


title: Aerial acoustic communications

abstract: This paper describes experiments in using audible sound as a means for wireless device communications. The direct application of standard modulation techniques to sound, without further improvements, results in sounds that are immediately perceived as digital communications and that are fairly aggressive and intrusive. We observe that some parameters of the modulation that have an impact on the data rate, the error probability and the computational overhead at the receiver also have a tremendous impact on the quality of the sound as perceived by humans. This paper focuses on how to vary those parameters in standard modulation techniques such as ASK, FSK and spread-spectrum to obtain communication systems in which the messages are musical and other familiar sounds, rather than modem sounds. A prototype called Digital Voices demonstrates the feasibility of this music-based communication technology. Our goal is to lay out the basis of sound design for aerial acoustic communications so that the presence of such communications, though noticeable, is not intrusive and can even be considered as part of musical compositions and sound tracks.


title: Electromyographic assessment of central motor disorders

abstract: Experimental procedures are described that were designed to assess central motor disorders quantitatively. Initially delineated is a study of triggered ballistic movement performed in a reaction-time situation. The reciprocal triphasic EMG pattern recorded in an antagonistic muscle pair was clearly abnormal in Parkinsonian patients; increased duration, diminished synchronization of motor units, and a tendency for coactivation of agonist and antagonist muscles and for action tremor were observed. Transport time of elbow movement was prolonged, particularly in a choice reaction-time situation. Rapid, passive displacements of the forearm combined with excitability measurements of hindlimb motoneurons in monkeys revealed the existence of a transcortical loop that may contribute to increased muscle tone in Parkinsonian patients.


title: Latent Dirichlet Allocation

abstract: We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.


title: Learning to Drive from Simulation without Real World Labels

abstract: Simulation can be a powerful tool for under-standing machine learning systems and designing methods to solve real-world problems. Training and evaluating methods purely in simulation is often “doomed to succeed” at the desired task in a simulated environment, but the resulting models are incapable of operation in the real world. Here we present and evaluate a method for transferring a vision-based lane following driving policy from simulation to operation on a rural road without any real-world labels. Our approach leverages recent advances in image-to-image translation to achieve domain transfer while jointly learning a single-camera control policy from simulation control labels. We assess the driving performance of this method using both open-loop regression metrics, and closed-loop performance operating an autonomous vehicle on rural and urban roads.


title: Binary Input Layer: Training of CNN models with binary input data

abstract: For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always excluded, as it leads to a significant error increase. Here, we present the novel concept of binary input layer (BIL), which allows the usage of binary input data by learning bit specific binary weights. The concept is evaluated on three datasets (PAMAP2, SVHN, CIFAR-10). Our results show that this approach is in particular beneficial for multimodal datasets (PAMAP2) where it outperforms networks using full precision weights in the first layer by 1:92 percentage points (pp) while consuming only 2 % of the chip area.


title: Forensic analysis of database tampering

abstract: Mechanisms now exist that detect tampering of a database, through the use of cryptographically-strong hash functions. This paper addresses the next problem, that of determining who, when, and what, by providing a systematic means of performing forensic analysis after such tampering has been uncovered. We introduce a schematic representation termed a "corruption diagram" that aids in intrusion investigation. We use these diagrams to fully analyze the original proposal, that of a linked sequence of hash values. We examine the various kinds of intrusions that are possible, including retroactive, introactive, backdating, and postdating intrusions. We then introduce successively more sophisticated forensic analysis algorithms: the monochromatic, RGB, and polychromatic algorithms, and characterize the "forensic strength" of these algorithms. We show how forensic analysis can efficiently extract a good deal of information concerning a corruption event.


title: A survey of mathematical methods for indoor localization

abstract: This document provides a survey of the mathematical methods currently used for position estimation in indoor local positioning systems (LPS), particularly those based on radiofrequency signals. The techniques are grouped into four categories: geometry-based methods, minimization of the cost function, fingerprinting, and Bayesian techniques. Comments on the applicability, requirements, and immunity to nonline-of-sight (NLOS) propagation of the signals of each method are provided.


title: Learning to identify emotions in text

abstract: This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and we propose and evaluate several knowledge-based and corpusbased methods for the automatic identification of these emotions in text.


title: SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

abstract: This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece.


title: Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

abstract: Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of operating in harsh environments without human supervision. The aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles.


title: Automatic Selection of Partitioning Variables for Small Multiple Displays

abstract: Effective small multiple displays are created by partitioning a visualization on variables that reveal interesting conditional structure in the data. We propose a method that automatically ranks partitioning variables, allowing analysts to focus on the most promising small multiple displays. Our approach is based on a randomized, non-parametric permutation test, which allows us to handle a wide range of quality measures for visual patterns defined on many different visualization types, while discounting spurious patterns. We demonstrate the effectiveness of our approach on scatterplots of real-world, multidimensional datasets.


title: Abstract Black-box and Gray-box Strategies for Virtual Machine Migration

abstract: Virtualization can provide significant benefits in data centers by enabling virtual machine migration to eliminate hotspots. We present Sandpiper, a system that automates the task of monitoring and detecting hotspots, determining a new mapping of physical to virtual resources and initiating the necessary migrations. Sandpiper implements a black-box approach that is fully OS- and application-agnostic and a gray-box approach that exploits OS- and application-level statistics. We implement our techniques in Xen and conduct a detailed evaluation using a mix of CPU, network and memory-intensive applications. Our results show that Sandpiper is able to resolve single server hotspots within 20 seconds and scales well to larger, data center environments. We also show that the gray-box approach can help Sandpiper make more informed decisions, particularly in response to memory pressure.


abstract: An archaeological project launched as part of Kofi Agorsah’s Fulbright Scholar program at the University of Cape Coast, Ghana in 2007, aimed at investigating the cultural formation and transformation of the historic Kormantse settlement on the Gold Coast, in response to changes occurring through colonial times. It sought to explain, by use of ethnographic and archaeological material, the processes and cultural manifestations by which the settlement’s population, including those who passed through Kormantse during the trans-Atlantic slave trade between the 16th and 20th centuries, negotiated their survival and identities. Preliminary studies in 1999 and 2007 indicate that Kormantse embraced the colonial slave trade and had access to abundant mass-produced local and foreign trade material and culture, while serving as a rallying point and an outlet for both the trans-Saharan and Atlantic trades. Bringing together archaeological assemblage, ethnographic and historical data to depict, explain and represent the numerous African populations identified under varying “Africa-named groups” in the colonial encounter in West Africa and their implications for the New World African cultures was the main challenge of this project. Specific issues addressed included identifying recognized material traces indicative of internal and external trade contacts and exchanges, migration routes and patterns of market traffic and, ultimately, the different groups represented in the colonial encounter with Kormantse and surrounding areas as the connecting links. It was expected that evidence of changing burial and other social practices as indices of the community’s shifting identity, would help determine how the communities in and around Kormantse adjusted to changing conditions of the colonial encounter. Artifact differentiation and sources of goods and “people,” travel routes, makers and makers’ marks on artifacts and scientific dates were main means of identifying and establishing how they related to emerging and continuing social distinctions within communities in and around Kormantse. 1 Agorsah and Butler: Archaeological Investigation of Historic Kormantse, Ghana: Cultur Published by ScholarWorks@UMass Amherst, 2014 2 Archaeological Investigation of Historic Kormantse, Ghana: Cultural Identities


title: Mining Efficient Association Rules Through Apriori Algorithm Using Attributes and Comparative Analysis of Various Association Rule Algorithms

abstract: In frequent pattern mining, there are several algorithms. Apriori is the classical and most famous algorithm. Objective of using Apriori algorithm is to find frequent itemsets and association between different itemsets i.e. association rule. In this paper, author considers data (bank data) and tries to obtain the result using Weka a data mining tool. Association rule algorithms are used to find out the best combination of different attributes in any data. In this paper author uses Apriori to find association rule. Here author consider three association rule algorithms: Apriori Association Rule, PredictiveApriori Association Rule and Tertius Association Rule. Author compares the result of these three algorithms and presents the result. According to the result obtained using data mining tool author find that Apriori Association algorithm performs better than the PredictiveApriori Association Rule and Tertius Association Rule algorithms.


title: Assumptions behind Intercoder Reliability Indices

abstract: Intercoder reliability is the most often used quantitative indicator of measurement quality in content studies. Researchers in psychology, sociology, education, medicine, marketing and other disciplines also use reliability to evaluate the quality of diagnosis, tests and other assessments. Many indices of reliability have been recommended for general use. This article analyzes 22, which are organized into 18 chance-adjusted and four non-adjusted indices. The chance-adjusted indices are further organized into three groups, including nine category-based indices, eight distribution-based indices, and one that is double based, on category and distribution. The main purpose of this work is to examine the assumptions behind each index. Most of the assumptions are unexamined in the literature, and yet these assumptions have implications for assessments of reliability that need to be understood, and that result in paradoxes and abnormalities. This article discusses 13 paradoxes and nine abnormalities to illustrate the 24 assumptions. To facilitate understanding, the analysis focuses on categorical scales with two coders, and further focuses on binary scales where appropriate. The discussion is situated mostly in analysis of communication content. The assumptions and patterns that we will discover will also apply to studies, evaluations and diagnoses in other disciplines with more coders, raters, diagnosticians, or judges using binary or multi-category scales. We will argue that a new index is needed. Before the new index can be established, we need guidelines for using the existing indices. This article will recommend such guidelines. ASSUMPTIONS BEHIND INTERCODER RELIABILITY INDICES 4 Communication Yearbook 36 Table of


title: Development of Computational Thinking Skills through Unplugged Activities in Primary School

abstract: Computational thinking is nowadays being widely adopted and investigated. Educators and researchers are using two main approaches to teach these skills in schools: with computer programming exercises, and with unplugged activities that do not require the use of digital devices or any kind of specific hardware. While the former is the mainstream approach, the latter is especially important for schools that do not have proper technology resources, Internet connections or even electrical power. However, there is a lack of investigations that prove the effectiveness of the unplugged activities in the development of computational thinking skills, particularly for primary schools. This paper, which summarizes a quasi-experiment carried out in two primary schools in Spain, tries to shed some light on this regard. The results show that students in the experimental groups, who took part in the unplugged activities, enhanced their computational thinking skills significantly more than their peers in the control groups who did not participate during the classes, proving that the unplugged approach may be effective for the development of this ability.


title: Application of Mathematical Model in Pension: System Reform for Retired Employee in Enterprise

abstract: In this paper, we describe the formatting guidelines for ACM SIG Proceedings. In this paper, the reform of the pensions system for retired workers in Chinese enterprises is studied by means of mathematical modeling. Firstly, the paper analyzes the average wage of workers in Shandong Province since 1978, which is rising as the time goes by using the statistical data and the Matlab software, combined with relevant knowledge of economics. The Malthus and Logistic models were successively adopted as empirical formulas for regression analysis, so as to obtain a more accurate prediction model. And the annual average wage of workers from 2019 to 2040 is predicted by using this model. Then, taking a certain enterprise as an example, a mathematical model is set up to calculate the pension substitution rate and the gap of the pension fund of the enterprise staff in all kinds of situations, and to give the age when the payment of the pension is in balance with the earnings. Finally, some effective measures for the government are given by using the model in order to achieve the target of 58.5% of the basic old-age insurance in the future and maintain the balance of income and expenditure of the pension insurance fund.


title: Artificial Bee Colony Algorithm applied to WiMAX network planning problem

abstract: WiMAX (Worldwide Interoperability for Microwave Access) or IEEE 802.16 is a technology developed to provide greater bandwidth and coverage. One of the most important challenges in wireless deploying network is the Base Stations (BS) location to meet traffic and coverage requirements efficiently. In this paper we are using a particularized swarm intelligence algorithm based on collective behavior of honey bees to find good solutions. In order to minimize the interference among cells and reduce energy consumption, we apply a post-processing to obtain power control and sectorization. Simulations results show that our method provides more efficient and better solutions than those are obtained by other evolutionary algorithms in the same instances.


title: Online learning in repeated auctions

abstract: Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased. We adopt an online learning approach with bandit feedback to model this problem and derive bidding strategies for two models: stochastic and adversarial. In the stochastic model, the observed values of the goods are random variables centered around the true value of the good. In this case, logarithmic regret is achievable when competing against well behaved adversaries. In the adversarial model, the goods need not be identical and we simply compare our performance against that of the best fixed bid in hindsight. We show that sublinear regret is also achievable in this case and prove matching minimax lower bounds. To our knowledge, this is the first complete set of strategies for bidders participating in auctions of this type.


title: Fast image dehazing using guided joint bilateral filter

abstract: In this paper, we propose a new fast dehazing method from single image based on filtering. The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also respect with depth information of the underlying image. We firstly obtain an initial atmosphere scattering light through median filtering, then refine it by guided joint bilateral filtering to generate a new atmosphere veil which removes the abundant texture information and recovers the depth edge information. Finally, we solve the scene radiance using the atmosphere attenuation model. Compared with exiting state of the art dehazing methods, our method could get a better dehazing effect at distant scene and places where depth changes abruptly. Our method is fast with linear complexity in the number of pixels of the input image; furthermore, as our method can be performed in parallel, thus it can be further accelerated using GPU, which makes our method applicable for real-time requirement.


title: Towards Commoditised Near Infrared Spectroscopy

abstract: Near Infrared Spectroscopy (NIRS) is a sensing technique in which near infrared light is transmitted into a sample, followed by light absorbance measurements at various wavelengths. This technique enables the inference of the inner chemical composition of the scanned sample, and therefore can be used to identify or classify objects. In this paper, we describe how to facilitate the use of NIRS by non- expert users in everyday settings. Our work highlights the key challenges of placing NIRS devices in the hands of non-experts. We develop a system to mitigate these challenges, and evaluate it in a user study. We show how NIRS technology can be successfully utilised by untrained users in an unsupervised manner through a special enclosure and an accompanying smartphone app. Finally, we discuss potential future developments of commoditised NIRS.


Implementing Intelligent Traffic Control System for Congestion Control , Ambulance Clearance , and Stolen Vehicle Detection


title: Korn’s inequalities for piecewise H¹ vector fields

abstract: Korn's inequalities for piecewise H 1 vector fields are established. They can be applied to classical nonconforming finite element methods, mortar methods and discontinuous Galerkin methods.


Public Review for Knowledge-Defined Networking


title: Fast fault detection, isolation and reconfiguration in fault-tolerant permanent magnet synchronous motor drives

abstract: This paper presents a variable speed ac drive based on a permanent magnet synchronous motor, supplied by a three-phase fault-tolerant power converter. With the aim to develop a fully automated fault-tolerant drive, special control routines were incorporated into the drive main controller that allow the control system to accomplish four important tasks, namely inverter fault diagnosis, faulty leg isolation, hardware reconfiguration and post-fault control optimization. The fault-tolerant control system integrates a reliable, fast and simple algorithm for real-time diagnostics of inverter open-circuit faults. This algorithm performs an important role since it is able to detect an inverter malfunction, and simultaneously providing information about its faulty phase. The control system acts in order to isolate the fault, performing then a hardware reconfiguration and a post-fault control optimization. By doing this, an inverter malfunction can be rapidly handled through a fast fault detection and reconfiguration process, minimizing the impact on the drive overall performance. Experimental results are presented showing the effectiveness of the developed fault-tolerant drive system.


title: A fan shaped triple band microstrip patch antenna with DGS for X-band applications

abstract: A slotted fan shaped microstrip patch antenna with three operating frequencies for radar application is presented. The design model is made to resonate at three frequencies in X-band. The X-band frequency range falls between 8 to 12 Gigahertz and are extensively used in radar applications. The dielectric substrate material used by the antenna is of low cost FR4 (Flame Retardant — Grade 4) Epoxy. To improve the antenna performance, the antenna design is included with Defected Ground Structure (DGS). The Ansoft High Frequency Structural Simulator (HFSS) Version 12 software is used to analyze the results. With 9.93 GHz, 10.81 GHz and 11.56 GHz as the resonant frequencies, a return loss of −18.47 dB, −42.23 dB and −19.74 dB are obtained with 470 MHz, 590 MHz and 420 MHz as the bandwidth respectively. The parameters like gain, bandwidth, return loss and directivity are discussed in the paper.


title: A Confidence-Aware Approach for Truth Discovery on Long-Tail Data

abstract: In many real world applications, the same item may be described by multiple sources. As a consequence, conflicts among these sources are inevitable, which leads to an important task: how to identify which piece of information is trustworthy, i.e., the truth discovery task. Intuitively, if the piece of information is from a reliable source, then it is more trustworthy, and the source that provides trustworthy information is more reliable. Based on this principle, truth discovery approaches have been proposed to infer source reliability degrees and the most trustworthy information (i.e., the truth) simultaneously. However, existing approaches overlook the ubiquitous long-tail phenomenon in the tasks, i.e., most sources only provide a few claims and only a few sources make plenty of claims, which causes the source reliability estimation for small sources to be unreasonable. To tackle this challenge, we propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. The proposed method not only estimates source reliability, but also considers the confidence interval of the estimation, so that it can effectively reflect real source reliability for sources with various levels of participation. Experiments on four real world tasks as well as simulated multi-source long-tail datasets demonstrate that the proposed method outperforms existing state-of-the-art truth discovery approaches by successful discounting the effect of small sources.


Using Data Mining to Combat Infrastructure Inefficiencies : The Case of Predicting Non-payment for Ethiopian Telecom


title: Online social relationships Page 1 The quality of online social relationships

abstract: Online relationships are less valuable than offline ones. Indeed, their net benefit depends on whether they supplement or substitute for offline social relationships.


title: CONCERNING SNS USE: HOW DO ISSUES OF PRIVACY AND TRUST CONCERN USERS?

abstract: As the popularity of social networking sites (SNSs) continues to grow, the research on what affects user intentions is also experiencing increased attention. This study investigates the effect of privacy and trust in other SNS users on trust in the SNS and the relationship between trust in the SNS, social norms and the intention to use an SNS. Data collected from online surveys were analyzed using linear regression analysis. The findings indicate that perceived privacy and trust in other SNS users has a positive relationship with trust in the SNS. However, the data showed no significant relationship between trust in the SNS and intention to use the SNS, unless mediated by social norms. This finding contributes to the literature on SNS intention to use, by confirming that social norms play a significant role in the way SNS users evaluate their intention to use.


title: Deep Learning in Robotics: A Review of Recent Research

abstract: Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-\`a-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.


title: Learning to Search with MCTSnets

abstract: Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. Among these algorithms, Monte-Carlo tree search (MCTS) is one of the most general, powerful and widely used. A typical implementation of MCTS uses cleverly designed rules, optimised to the particular characteristics of the domain. These rules control where the simulation traverses, what to evaluate in the states that are reached, and how to back-up those evaluations. In this paper we instead learn where, what and how to search. Our architecture, which we call an MCTSnet, incorporates simulation-based search inside a neural network, by expanding, evaluating and backing-up a vector embedding. The parameters of the network are trained end-to-end using gradient-based optimisation. When applied to small searches in the well-known planning problem Sokoban, the learned search algorithm significantly outperformed MCTS baselines.


title: Synthesizing evidence on complex interventions: how meta-analytical, qualitative, and mixed-method approaches can contribute.

abstract: OBJECTIVES ::: Although there is increasing interest in the evaluation of complex interventions, there is little guidance on how evidence from complex interventions may be reviewed and synthesized, and the relevance of the plethora of evidence synthesis methods to complexity is unclear. This article aims to explore how different meta-analytical approaches can be used to examine aspects of complexity; describe the contribution of various narrative, tabular, and graphical approaches to synthesis; and give an overview of the potential choice of selected qualitative and mixed-method evidence synthesis approaches. ::: ::: ::: STUDY DESIGN AND SETTING ::: The methodological discussions presented here build on a 2-day workshop held in Montebello, Canada, in January 2012, involving methodological experts from the Campbell and Cochrane Collaborations and from other international review centers (Anderson L, Petticrew M, Chandler J, et al. ::: ::: ::: INTRODUCTION ::: systematic reviews of complex interventions. In press). These systematic review methodologists discussed the broad range of existing methods and considered the relevance of these methods to reviews of complex interventions. ::: ::: ::: RESULTS ::: The evidence from primary studies of complex interventions may be qualitative or quantitative. There is a wide range of methodological options for reviewing and presenting this evidence. Specific contributions of statistical approaches include the use of meta-analysis, meta-regression, and Bayesian methods, whereas narrative summary approaches provide valuable precursors or alternatives to these. Qualitative and mixed-method approaches include thematic synthesis, framework synthesis, and realist synthesis. A suitable combination of these approaches allows synthesis of evidence for understanding complex interventions. ::: ::: ::: CONCLUSION ::: Reviewers need to consider which aspects of complex interventions should be a focus of their review and what types of quantitative and/or qualitative studies they will be including, and this will inform their choice of review methods. These may range from standard meta-analysis through to more complex mixed-method synthesis and synthesis approaches that incorporate theory and/or user's perspectives.


title: Critical patient eHealth monitoring system using wearable sensors

abstract: Patient monitoring has advanced over the years, from bed side monitors in the hospital, to wearable devices that can monitor patients and communicate their data remotely to medical servers over wireless networks. It is a process that involves monitoring major vital signs of a patient, to check if their health is normal or deteriorating within a period of time. In a remote situation, vital signs information, can help health care providers to easily send help to patients when their health is at immediate risk. The problem with this kind of remote monitoring system is that most times the patients must be within a specified location to either monitor their health or receive emergency help. This paper presents a potential solution in the form of a global vital sign monitoring system and consists of two components to demonstrate the functionality; a wearable wireless monitoring device that records the temperature and pulse rate of the patient wearing it and a web application, which allows the patient and the emergency response unit to interact together over cellular network.


title: A smart watch-based gesture recognition system for assisting people with visual impairments

abstract: Modern mobile devices provide several functionalities and new ones are being added at a breakneck pace. Unfortunately browsing the menu and accessing the functions of a mobile phone is not a trivial task for visual impaired users. Low vision people typically rely on screen readers and voice commands. However, depending on the situations, screen readers are not ideal because blind people may need their hearing for safety, and automatic recognition of voice commands is challenging in noisy environments. Novel smart watches technologies provides an interesting opportunity to design new forms of user interaction with mobile phones. We present our first works towards the realization of a system, based on the combination of a mobile phone and a smart watch for gesture control, for assisting low vision people during daily life activities. More specifically we propose a novel approach for gesture recognition which is based on global alignment kernels and is shown to be effective in the challenging scenario of user independent recognition. This method is used to build a gesture-based user interaction module and is embedded into a system targeted to visually impaired which will also integrate several other modules. We present two of them: one for identifying wet floor signs, the other for automatic recognition of predefined logos.


title: Script independent scene text segmentation using fast stroke width transform and GrabCut

abstract: In this paper, a novel script independent scene text segmentation method is presented. The proposed method introduces an approach for character segmentation which combines the advantages of connected component analysis, stroke width analysis and graph cuts. The method was evaluated on a standard dataset, and also on a custom dataset of scene images of text from various Indian scripts. Additionally, a fast implementation of Stroke Width Transform (SWT) is presented here, which improves speed over other implementations by 79%. From our experiments, we have achieved encouraging results on the usefulness of the combination of text/non-text classification with GrabCut. The technique is robust to the language/script of text.


title: Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes

abstract: Introduction of synthetic circuits into host microbes creates competition between circuit and host genes for shared cellular resources, such as RNA polymerases and ribosomes. This can lead to the emergence of unwanted coupling between the expression of different genes, complicating circuit design and potentially leading to circuit failure. Here we demonstrate the ability of orthogonal ribosomes to alleviate the effects of this resource competition. We partition the ribosome pool by expressing an engineered 16S RNA with altered specificity, and use this division of specificity to build simple resource allocators which reduce the level of ribosome-mediated gene coupling. We then design and implement a dynamic resource allocation controller, which acts to increase orthogonal ribosome production as the demand for translational resources by a synthetic circuit increases. Our results highlight the potential of dynamic translational resource allocation as a means of minimising the impact of cellular limitations on the function of synthetic circuitry.


title: Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings

abstract: Domain generation algorithms (DGAs) are frequently employed by malware to generate domains used for connecting to command-and-control (C2) servers. Recent work in DGA detection leveraged deep learning architectures like convolutional neural networks (CNNs) and character-level long short-term memory networks (LSTMs) to classify domains. However, these classifiers perform poorly with wordlist-based DGA families, which generate domains by pseudorandomly concatenating dictionary words. We propose a novel approach that combines context-sensitive word embeddings with a simple fully-connected classifier to perform classification of domains based on word-level information. The word embeddings were pre-trained on a large unrelated corpus and left frozen during the training on domain data. The resulting small number of trainable parameters enabled extremely short training durations, while the transfer of language knowledge stored in the representations allowed for high-performing models with small training datasets. We show that this architecture reliably outperformed existing techniques on wordlist-based DGA families with just 30 DGA training examples and achieved state-of-the-art performance with around 100 DGA training examples, all while requiring an order of magnitude less time to train compared to current techniques. Of special note is the technique's performance on the matsnu DGA: the classifier attained a 89.5% detection rate with a 1:1,000 false positive rate (FPR) after training on only 30 examples of the DGA domains, and a 91.2% detection rate with a 1:10,000 FPR after 90 examples. Considering that some of these DGAs have wordlists of several hundred words, our results demonstrate that this technique does not rely on the classifier learning the DGA wordlists. Instead, the classifier is able to learn the semantic signatures of the wordlist-based DGA families.


title: Number Plate Recognition for use in different countries using an improved segmentation

abstract: Automatic Number Plate Recognition (ANPR) is a real time embedded system which identifies the characters directly from the image of the license plate. It is an active area of research. ANPR systems are very useful to the law enforcement agencies as the need for Radio Frequency Identification tags and similar equipments are minimized. Since number plate guidelines are not strictly practiced everywhere, it often becomes difficult to correctly identify the non-standard number plate characters. In this paper we try to address this problem of ANPR by using a pixel based segmentation algorithm of the alphanumeric characters in the license plate. The non-adherence of the system to any particular country-specific standard & fonts effectively means that this system can be used in many different countries — a feature which can be especially useful for trans-border traffic e.g. use in country borders etc. Additionally, there is an option available to the end-user for retraining the Artificial Neural Network (ANN) by building a new sample font database. This can improve the system performance and make the system more efficient by taking relevant samples. The system was tested on 150 different number plates from various countries and an accuracy of 91.59% has been reached.


TT virus infection in Brazilian Amazon Indians and comparison with non-Indian subjects


Teachers and human rights education


Gleaning Wisdom from the Past: Early Detection of Emerging Rumors in Social Media


title: Malware detection based on ontology

abstract: Malware in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. So how to describe the behavior knowledge of malware is an interesting and meaningful work. In recent years, different ontology technologies have been proposed to represent domain knowledge. In the study, we apply ontology techniques into the field of malware detection, and propose the malware detection method based on ontology. This method is based on the behavior of malicious code, and makes a knowledge representation of the malware behaviors from a variety of perspectives. We use the common behaviors of individuals to represent the behaviors of a malware family, and use the ontology reasoning mechanism to detect unknown malware samples. Experiments show that the method has high malicious code detection rate and low false alarm rate.


title: Diversity in software engineering research

abstract: One of the goals of software engineering research is to achieve generality: Are the phenomena found in a few projects reflective of others? Will a technique perform as well on projects other than the projects it is evaluated on? While it is common sense to select a sample that is representative of a population, the importance of diversity is often overlooked, yet as important. In this paper, we combine ideas from representativeness and diversity and introduce a measure called sample coverage, defined as the percentage of projects in a population that are similar to the given sample. We introduce algorithms to compute the sample coverage for a given set of projects and to select the projects that increase the coverage the most. We demonstrate our technique on research presented over the span of two years at ICSE and FSE with respect to a population of 20,000 active open source projects monitored by Ohloh.net. Knowing the coverage of a sample enhances our ability to reason about the findings of a study. Furthermore, we propose reporting guidelines for research: in addition to coverage scores, papers should discuss the target population of the research (universe) and dimensions that potentially can influence the outcomes of a research (space).


title: Principles of first trimester screening in the age of non-invasive prenatal diagnosis: screening for other major defects and pregnancy complications

abstract: Purpose ::: First trimester risk assessment plays a major role in the contemporary pregnancy care. It has evolved significantly since its introduction in the 1990s when it essentially consisted of just the nuchal translucency measurement. Today, it involves the measurement of several biophysical and biochemical markers and can assess the risk for a wide array of major chromosomal and non-chromosomal defects as well as other pregnancy-related complications.


title: Content-boosted Matrix Factorization Techniques for Recommender Systems

abstract: Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013


title: Junction detection in handwritten documents and its application to writer identification

abstract: In this paper, we propose a novel junction detection method in handwritten images, which uses the stroke-length distribution in every direction around a reference point inside the ink of texts. Our proposed junction detection method is simple and efficient, and yields a junction feature in a natural manner, which can be considered as a local descriptor. We apply our proposed junction detector to writer identification by Junclets which is a codebook-based representation trained from the detected junctions. A new challenging data set which contains multiple scripts (English and Chinese) written by the same writers is introduced to evaluate the performance of the proposed junctions for cross-script writer identification. Furthermore, two other common data sets are used to evaluate our junction-based descriptor. Experimental results show that our proposed junction detector is stable under rotation and scale changes, and the performance of writer identification indicates that junctions are important atomic elements to characterize the writing styles. The proposed junction detector is applicable to both historical documents and modern handwritings, and can be used as well for junction retrieval. HighlightsThis paper describes a simple and efficient junction detection method in handwritten documents.The proposed junction detection method yields a junction feature in a general manner.The proposed junction features and Junclets were applied to writer identification and junction retrieval.


title: Automatic Modeling of Musical Style

abstract: In this paper, we describe and compare two methods for unsupervised learning of musical style, both of which perform analyses of musical sequences and then compute a model from which new interpretations / improvisations close to the original's style can be generated. In both cases, an important part of the musical structure is captured, including rhythm, melodic contour, and polyphonic relationships. The first method is a drastic improvement of the Incremental Parsing (IP) method, a method derived from compression theory and proven useful in the musical domain. The second one is an application to music of Prediction Suffix Trees (PST), a learning technique initially developed for statistical modeling of complex sequences with applications in linguistics and biology.


title: A hybrid edge-cloud architecture for reducing on-demand gaming latency

abstract: The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.


title: Prevalence of Chlamydia trachomatis and Neisseria gonorrhoeae infection in pediatric private practice.

abstract: BACKGROUND ::: Universal screening of sexually active adolescents for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) has been recommended in settings in which prevalence is 2% or greater. Although believed to be above 2%, the prevalence of CT and GC infection in private practice settings has not been clearly established and may affect screening practices. ::: ::: ::: OBJECTIVES ::: To determine CT and GC infection prevalence in 2 pediatric private practices. ::: ::: ::: DESIGN ::: Cross-sectional study. ::: ::: ::: SETTING ::: Two pediatric private practices in suburban North Carolina. ::: ::: ::: PATIENTS ::: Convenience sample of patients aged 15 to 24 years who were seen from August 1998 through June 1999. ::: ::: ::: MAIN OUTCOME MEASURES ::: Prevalence of CT and GC infection. ::: ::: ::: RESULTS ::: Of 1114 eligible patients, 803 (72%) completed questionnaires and provided urine specimens tested for CT and GC infection using ligase chain reaction assays. Mean age was 17.1 years (standard deviation: 1.8). Most participants were female (63%), white (87%), and from highly educated families (64% of their mothers graduated from college). Sexual activity was reported by 41%. Prevalence of CT infection in all participants was 0.9% (confidence interval [CI]: 0.4%-1.8%); in sexually active participants, 2.1% (CI: 0.9%-4.3%); in sexually active females, 2.7% (CI: 1.0%-5.7%); and in sexually active males, 0.9% (CI: 0.0%-5.1%). One case of GC infection was found. ::: ::: ::: CONCLUSIONS ::: The prevalence of CT and GC infection in this private practice population was much lower than reported in other settings. Screening recommendations may need to be reassessed if other low prevalence populations are found.


title: Compact Hybrid Resonator With Series and Shunt Resonances Used in Miniaturized Filters and Balun Filters

abstract: A hybrid resonant circuit is proposed in this paper. The circuit is a combination of a shunt resonant circuit and series resonant circuit. With this combination, lower resonant frequency is achieved as compared to the single shunt and series resonant circuits. As a result, a compact resonator with smaller size can be achieved as compared to the conventional quarter- and half-wave resonators. Besides the size reduction, the proposed resonant circuit is able to introduce a transmission zero to improve the stopband suppression in filter design. Based on this circuit, a very compact interdigital coupled microstrip resonator is proposed in this paper. The resonator achieves a small length of nearly 1/10 guided wavelength (?g), which has a length reduction of 63% as compared to the conventional uniform quarter-wave resonator. By using the proposed resonator, a second-order bandpass filter with a small size of 0.144?g × 0.128?g and a fourth-order bandpass filter with a size of 0.217?g × 0.1?g are built based on the standard filter synthesis methods. Both good performance and miniaturization are achieved for the proposed filters, and the expected transmission zeros are also observed. In addition to the small filters, the proposed resonator is suitable for miniaturized balun bandpass filters. A novel configuration for a balun bandpass filter is proposed based on the aforementioned resonators. A second-order balun bandpass filter with a size of 0.26?g × 0.145?g and afourth-order balun bandpass filter with a size of 0.213?g × 0.203?g are reported in this paper. Both balun filters achieve good filtering performance, as well as excellent amplitude and phase imbalances, which are less than 1 dB and 1° in the passband, respectively.


title: Effects of Professional Development on Teachers' Instruction: Results from a Three-year Longitudinal Study

abstract: This article examines the effects of professional development on teachers’ instruction. Using a purposefully selected sample of about 207 teachers in 30 schools, in 10 districts in five states, we examine features of teachers’ professional development and its effects on changing teaching practice in mathematics and science from 1996–1999. We found that professional development focused on specific instructional practices increases teachers’ use of those practices in the classroom. Furthermore, we found that specific features, such as active learning opportunities, increase the effect of the professional development on teacher’s instruction.


title: Learning Fuzzy Relations and Properties for Explainable Artificial Intelligence

abstract: The goal of explainable artificial intelligence is to solve problems in a way that humans can understand how it does it. However, few approaches have been proposed so far and some of them lay more emphasis on interpretability than on explainability. In this paper, we propose an approach that is based on learning fuzzy relations and fuzzy properties. We extract frequent relations from a dataset to generate an explained decision. Our approach can deal with different problems, such as classification or annotation. A model was built to perform explained classification on a toy dataset that we generated. It managed to correctly classify examples while providing convincing explanations. A few areas for improvement have been spotted, such as the need to filter relations and properties before or while learning them in order to avoid useless computations.


title: A 1.2 V Low-Power CMOS Chopper-Stabilized Analog Front-End IC for Glucose Monitoring

abstract: A low-power 1.2 V CMOS chopper-stabilized analog front-end integrated circuit (IC) for glucose monitoring is presented in this letter. The operating parameters of the IC, including reference voltage in potentiostat, current offset, output gain, and offset, are fully programmable. The IC is fabricated using 0.13- $\mu \text{m}$ CMOS technology, has an active area of $1.4\,\,\text {mm} \times 4.3$ mm, and its power consumption is $30.2~\mu \text{W}$ . Furthermore, a chopper-stabilized open-loop transimpedance amplifier is proposed for low-power and low-noise implementation. The integrated input-referred current noise is 260 pArms with a bandwidth of 100 Hz.


title: Error Correlation Prediction in Lockstep Processors for Safety-Critical Systems

abstract: This paper presents a new phenomenon called error correlation prediction for lockstep processors. Lockstep processors run the same copy of a program, and their outputs are compared at every cycle to detect divergence, and have been popular in safety-critical systems. When the lockstep error checker detects an error, it alerts the safety-critical system by putting the lockstep processor in a safe state in order to prevent hazards. This is done by running the online diagnostics to identify the cause of the error because the lockstep processor has no knowledge of whether the error is caused by a transient or permanent fault. The online diagnostics can be avoided if the error is caused by a transient fault, and the lockstep processor can recover from it. If, however, it is caused by a permanent fault, having prior knowledge about error's likely location(s) within the CPU speeds up the diagnostics process. We discover that the error's type and likely location(s) inside CPUs from which the fault may have originated can be predicted by analyzing the output signals of the CPU(s) when the error is detected. We design a simple static predictor exploiting this phenomenon and show that system availability can be increased by 42-64% with an overhead of less than 2% in silicon area and power.


title: A Survey on Plant Disease Detection using Support Vector Machine

abstract: Agriculture is an important source of livelihood and Indian economy depends on agricultural production. It is important to detect the plant leaf diseases at early stage to increase the crop yield and profit. Image processing technique is used to detect the leaf diseases accurately since naked eye observation of the diseases does not provide accurate result all the time especially during the early stage. It was done in five phases which are image acquisition, preprocessing of the acquired image, feature extraction, classification of the diseases and displaying the result. This paper provides a detailed survey about classification of the agricultural diseases by using Support Vector Machine classifier.


title: Learning human behaviors from motion capture by adversarial imitation

abstract: Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies. However, methods that use pure reinforcement learning with simple reward functions tend to produce non-humanlike and overly stereotyped movement behaviors. In this work, we extend generative adversarial imitation learning to enable training of generic neural network policies to produce humanlike movement patterns from limited demonstrations consisting only of partially observed state features, without access to actions, even when the demonstrations come from a body with different and unknown physical parameters. We leverage this approach to build sub-skill policies from motion capture data and show that they can be reused to solve tasks when controlled by a higher level controller.


title: Selection of relevant features and examples in machine learning

abstract: Abstract In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.


Resolving the Pedagogical Disconnect between User Stories and Use Cases in Systems Analysis and Design Textbooks.


Associative Classification Mining for Website Phishing Classification


title: Deep Metric Learning for Crowdedness Regression

abstract: Cross-scene regression tasks, such as congestion level detection and crowd counting, are useful but challenging. There are two main problems, which limit the performance of existing algorithms. The first one is that no appropriate congestion-related feature can reflect the real density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks, since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information, such as a density map, to guide the learning, which increases the effort of labeling. Another problem is that most existing methods are composed of several steps, for example, feature extraction and regression. Since the steps in the pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning-based regression method is proposed to extract density related features, and learn better distance measurement simultaneously. The proposed networks trained end-to-end for better optimization can be used for crowdedness regression tasks, including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method.


Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences.


title: Visualizing multidimensional cancer genomics data

abstract: Cancer genomics projects employ high-throughput technologies to identify the complete catalog of somatic alterations that characterize the genome, transcriptome and epigenome of cohorts of tumor samples. Examples include projects carried out by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). A crucial step in the extraction of knowledge from the data is the exploration by experts of the different alterations, as well as the multiple relationships between them. To that end, the use of intuitive visualization tools that can integrate different types of alterations with clinical data is essential to the field of cancer genomics. Here, we review effective and common visualization techniques for exploring oncogenomics data and discuss a selection of tools that allow researchers to effectively visualize multidimensional oncogenomics datasets. The review covers visualization methods employed by tools such as Circos, Gitools, the Integrative Genomics Viewer, Cytoscape, Savant Genome Browser, StratomeX and platforms such as cBio Cancer Genomics Portal, IntOGen, the UCSC Cancer Genomics Browser, the Regulome Explorer and the Cancer Genome Workbench.


title: Expert Gate: Lifelong Learning with a Network of Experts

abstract: In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with fine-tuning or learning-without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.


title: TITAN: Test Suite Optimization for Highly Configurable Software

abstract: Exhaustive testing of highly configurable software developed in continuous integration is rarely feasible in practice due to the configuration space of exponential size on the one hand, and strict time constraints on the other. This entails using selective testing techniques to determine the most failure-inducing test cases, conforming to highly-constrained time budget. These challenges have been well recognized by researchers, such that many different techniques have been proposed. In practice, however, there is a lack of efficient tools able to reduce high testing effort, without compromising software quality. In this paper we propose a test suite optimization technology TITAN, which increases the time-and cost-efficiency of testing highly configurable software developed in continuous integration. The technology implements practical test prioritization and minimization techniques, and provides test traceability and visualization for improving the quality of testing. We present the TITAN tool and discuss a set of methodological and technological challenges we have faced during TITAN development. We evaluate TITAN in testing of Cisco's highly configurable software with frequent high quality releases, and demonstrate the benefit of the approach in such a complex industry domain.


title: Cyclic amide derivatives as potential prodrugs II: N-hydroxymethylsuccinimide- / isatin esters of some NSAIDs as prodrugs with an improved therapeutic index.

abstract: Abstract Ester prodrugs of aspirin 1a , ibuprofen 1b , naproxen 1c and indomethacin 1d were synthesized using N -Hydroxymethylsuccinimide (HMSI) 3 and N -hydroxymethylisatin (HMIS) 4 as promoieties to reduce their gastrointestinal toxicity and improve bioavailability. Additionally, the kinetics of hydrolysis of the synthesized prodrugs 5a–d and 6a–d were studied at 37 °C in non-enzymatic simulated gastric fluid (SGF; hydrochloric acid buffer pH = 1.2); 0.02 M phosphate buffer (pH = 7.4); 80% human plasma and 10% rat liver homogenate. The results indicate higher chemical stability of the ester prodrugs in non-enzymatic SGF (t 1/2 ≅ 6.5–18.6 h) and rapid conversion to the parent drugs in 80% human plasma (t 1/2 ≅ 11.4–235 min) as well as in 10% rat liver homogenates (t 1/2 ≅ 12.0–90.0 min). As a general pattern, the HMSI esters 5a–d revealed higher chemical stability than the corresponding HMIS analogues 6a–d. The pH-rate profile of 5c and 6a indicated maximum stability of the former at pH = 1.2–8.0 and of the latter at pH = 1.2–4.0. The distribution coefficient (D 7.4 ) values of the prodrugs 5a–d, 6a–d and the parent drugs 1a–d in an n-octanol/phosphate buffer (pH = 7.4) system indicated enhanced lipophilic properties of the prodrugs. Furthermore, the HMIS ester prodrugs 6a–d are more lipophilic than the corresponding HMSI derivatives 5a–d . In vivo ulcerogenicity studies using scanning electron microscopy on stomach specimens of rats treated with an oral dose for 4 d revealed that the synthesized ester prodrugs are significantly less irritating to gastric mucosa than the parent drugs. These results suggested HMSI and/or HMIS esters possess good potential as prodrugs with an improved therapeutic index for oral delivery of NSAIDs.


title: Pedestrian-cyclist Interactions at Bus Stops along Segregated Bike Paths : A Case Study of Montreal Authors :

abstract: Safety is one of the major world health issues, and is even more acute for “vulnerable” road users, pedestrians and cyclists. At the same time, public authorities are promoting the active modes of transportation that involve these very users for their health benefits. It is therefore important to understand the factors and designs that provide the best safety for vulnerable road users and encourage more people to use these modes. Qualitative and quantitative shortcomings of collisions make it necessary to use surrogate measures of safety in studying these modes. Some interactions without a collision such as conflicts can be good surrogates of collisions as they are more frequent and less costly. To overcome subjectivity and reliability challenges, automatic conflict analysis using video cameras and deriving users’ trajectories is a solution to overcome shortcomings of manual conflict analysis. The goal of this paper is to identify and characterize various interactions between cyclists and pedestrians at bus stops along bike paths using a fully automated process. Three conflict severity indicators are calculated and adapted to the situation of interest to capture those interactions. A microscopic analysis of users’ behavior is proposed to explain interactions more precisely. Eventually, the study aims to show the capability of automatically collecting and analyzing data for pedestrian-cyclist interactions at bus stops along segregated bike paths in order to better understand the actual and perceived risks of these facilities.


title: Towards In-baggage Suspicious Object Detection Using Commodity WiFi

abstract: The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the finegrained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous (i.e., defined as any metal and liquid object) without penetrating into the user’s privacy through physically opening the baggage. Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object’s dimension (i.e., liquid volume and metal object’s shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively.


title: Data Security and Privacy: Concepts, Approaches, and Research Directions

abstract: Data are today an asset more critical than ever for all organizations we may think of. Recent advances and trends, such as sensor systems, IoT, cloud computing, and data analytics, are making possible to pervasively, efficiently, and effectively collect data. However for data to be used to their full power, data security and privacy are critical. Even though data security and privacy have been widely investigated over the past thirty years, today we face new difficult data security and privacy challenges. Some of those challenges arise from increasing privacy concerns with respect to the use of data and from the need of reconciling privacy with the use of data for security in applications such as homeland protection, counterterrorism, and health, food and water security. Other challenges arise because the deployments of new data collection and processing devices, such as those used in IoT systems, increase the data attack surface. In this paper, we discuss relevant concepts and approaches for data security and privacy, and identify research challenges that must be addressed by comprehensive solutions to data security and privacy.


title: Building theory in the sciences of the artificial

abstract: This essay extends Simon's arguments in the Sciences of the Artificial to a critical examination of how theorizing in Information Technology disciplines should occur. The essay is framed around a number of fundamental questions that relate theorizing in the artificial sciences to the traditions of the philosophy of science. Theorizing in the artificial sciences is contrasted with theorizing in other branches of science and the applicability of the scientific method is questioned. The paper argues that theorizing should be considered in a holistic manner that links two modes of theorizing: an interior mode with the how of artifact construction studied and an exterior mode with the what of existing artifacts studied. Unlike some representations in the design science movement the paper argues that the study of artifacts once constructed can not be passed back uncritically to the methods of traditional science. Seven principles for creating knowledge in IT disciplines are derived: (i) artifact system centrality; (ii) artifact purposefulness; (iii) need for design theory; (iv) induction and abduction in theory building; (v) artifact construction as theory building; (vi) interior and exterior modes for theorizing; and (viii) issues with generality. The implicit claim is that consideration of these principles will improve knowledge creation and theorizing in design disciplines, for both design science researchers and also for researchers using more traditional methods. Further, attention to these principles should lead to the creation of more useful and relevant knowledge.


title: Genome-wide association study reveals putative regulators of bioenergy traits inPopulus deltoides

abstract: Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. These polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.


If your bug database could talk


title: ABSTRACT Linking Social Networks on the Web with FOAF

abstract: One of the core goals of the Semantic Web is to store data in distributed locations, and use ontologies and reasoning to aggregate it. Social networking is a large movement on the web, and social networking data using the Friend of a Friend (FOAF) vocabulary makes up a significant portion of all data on the Semantic Web. Many traditional webbased social networks share their members’ information in FOAF format. While this is by far the largest source of FOAF online, there is no information about whether the social network models from each network overlap to create a larger unified social network model, or whether they are simply isolated components. In this paper, we present a study of the intersection of FOAF data found in many online social networks. Using the semantics of the FOAF ontology and applying Semantic Web reasoning techniques, we show that a significant percentage of profiles can be merged from multiple networks. We present results on how this aects network structure and what it says about relationships and individual behavior. Finally, we discuss the implications this has for using web-based social networking data to create intelligent user interfaces and social software.


title: PBCert: Privacy-Preserving Blockchain-Based Certificate Status Validation Toward Mass Storage Management

abstract: In the recent years, the vulnerabilities of conventional public key infrastructure are exposed by the real-world attacks, such as the certificate authority’s single-point-of-failure or clients’ private information leakage. Aimed at the first issue, one type of approach is that multiple entities are introduced to assist the certificate operations, including registration, update, and revocation. However, it is inefficient in computation. Another type is to make the certificate information publicly visible by bringing in the log servers. Nevertheless, the data synchronization among log servers may lead to network latency. Based on the second approach, the blockchain-based public key infrastructure schemes are proposed. Through these type of schemes, all the certificate operations are stored in the blockchain for public audit. However, the issue of revoked certificates’ status storage is worth paying attention, especially in the setting with massive certificates. In addition, the target web server that a client wants to access is exposed in the process of certificate status validation. In this paper, we propose a privacy-preserving blockchain-based certificate status validation scheme called PBCert to solve these two issues. First, we separate the revoked certificates control and storage plane. Only the minimal control information (namely, certificate hashes and related operation block height) is stored in the blockchain and it uses external data stores for the detailed information about all revoked certificates. Second, we design an obscure response to the clients’ certificate status query for the purpose of privacy preserving. Through the security analysis and experiment evaluation, our scheme is significant in practice.


title: Combining deep learning and unsupervised clustering to improve scene recognition performance

abstract: Deep Neural Networks (DNN) are now the state-of-the-art for many image and object recognition tasks, as illustrated by their performance on standard benchmarks. The success of DNNs is attributed to their ability to learn rich mid-level image representations, as opposed to hand-designed low-level features used in other image analysis methods. Typically a large dataset of unlabeled images is used for unsupervised feature learning, and then standard classifiers are trained on the features extracted from the images in a labeled set. In this paper, we show that clustering the images using the features from the DNN allows more accurate per-cluster classifiers to be learned, which improves the overall classification accuracy. We demonstrate the effectiveness of our approach on a scene recognition task.


title: An application of chain code-based local descriptor and its extension to face recognition

abstract: Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstrate that the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm. An extension of Chain Code-Based Local Descriptor (CCBLD) is proposed.CCBLD is applied to face recognition task.Bag-of-Visual-Words paradigm is realized through the dictionary of chain-codes.Test results show that CCBLD is comparable or outperforms other local descriptors.The approach is tested using CAS-PEAL, ColorFERET, FG-NET, and other datasets.


title: A revised examination of the dual pathway model for bulimic symptoms: The importance of social comparisons made on Facebook and sociotropy

abstract: Objective ::: To replicate the Dual Pathway Model (DPM) of bulimia nervosa (BN) symptoms prospectively, and to assess whether a revised version of the DPM that included the variables social comparisons made on Facebook and sociotropy influenced the DPM.


title: EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

abstract: Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify. Existing methods for crafting adversarial examples are based on $L_2$ and $L_\infty$ distortion metrics. However, despite the fact that $L_1$ distortion accounts for the total variation and encourages sparsity in the perturbation, little has been developed for crafting $L_1$-based adversarial examples. In this paper, we formulate the process of attacking DNNs via adversarial examples as an elastic-net regularized optimization problem. Our elastic-net attacks to DNNs (EAD) feature $L_1$-oriented adversarial examples and include the state-of-the-art $L_2$ attack as a special case. Experimental results on MNIST, CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial examples with small $L_1$ distortion and attains similar attack performance to the state-of-the-art methods in different attack scenarios. More importantly, EAD leads to improved attack transferability and complements adversarial training for DNNs, suggesting novel insights on leveraging $L_1$ distortion in adversarial machine learning and security implications of DNNs.


title: Plushie: an interactive design system for plush toys

abstract: We introduce Plushie, an interactive system that allows nonprofessional users to design their own original plush toys. To design a plush toy, one needs to construct an appropriate two-dimensional (2D) pattern. However, it is difficult for non-professional users to appropriately design a 2D pattern. Some recent systems automatically generate a 2D pattern for a given three-dimensional (3D) model, but constructing a 3D model is itself a challenge. Furthermore, an arbitrary 3D model cannot necessarily be realized as a real plush toy, and the final sewn result can be very different from the original 3D model. We avoid this mismatch by constructing appropriate 2D patterns and applying simple physical simulation to it on the fly during 3D modeling. In this way, the model on the screen is always a good approximation of the final sewn result, which makes the design process much more efficient. We use a sketching interface for 3D modeling and also provide various editing operations tailored for plush toy design. Internally, the system constructs a 2D cloth pattern in such a way that the simulation result matches the user's input stroke. Our goal is to show that relatively simple algorithms can provide fast, satisfactory results to the user whereas the pursuit of optimal layout and simulation accuracy lies outside this paper's scope. We successfully demonstrated that non-professional users could design plush toys or balloon easily using Plushie.


title: Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies.

abstract: Muscular sound is a mechanical phenomenon detectable at the surface of an active muscle, which has been known and described since 1800. Only recently, because of the availability of reliable transducers and sophisticated analysis techniques, has this signal become attractive for monitoring the mechanical aspects of muscle contraction. The muscular sound characteristics were investigated both during electrically elicited and voluntary contractions. In the first case, the influence of the biophysical and mechanical properties of the muscle on this signal was studied. During voluntary efforts the summation of the mechanical activity of the recruited motor units was analyzed. The results indicate that the muscular sound may be an adjunct tool to the force, the physiological force tremor, and electromyogram to obtain information on the muscle mechanical model as well as on muscle motor control. This review focuses on the following aspects of the signal: (1) recording problems; (2) muscle sound properties during stimulation of isolated and in vivo muscle; (3) time and frequency domain analysis during non fatiguing and fatiguing contractions; (4) comparison with other signals related to muscle activity; (5) discussion on the origin; and (6) possible practical applications.


title: A Berkeley View of Systems Challenges for AI

abstract: With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production. These changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems software and architectures, and by the broad accessibility of these technologies. ::: The next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (often mission-critical) decisions on our behalf, often in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising confidentiality. These challenges will be exacerbated by the end of the Moore's Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI's potential to improve lives and society.