GUITAR: Piecing Together Android App GUIs from Memory Images

An Android app's graphical user interface (GUI) displays rich semantic and contextual information about the smartphone's owner and app's execution. Such information provides vital clues to the investigation of crimes in both cyber and physical spaces. In real-world digital forensics however, once an electronic device becomes evidence most manual interactions with it are prohibited by criminal investigation protocols. Hence investigators must resort to "image-and-analyze" memory forensics (instead of browsing through the subject phone) to recover the apps' GUIs. Unfortunately, GUI reconstruction is still largely impossible with state-of-the-art memory forensics techniques, which tend to focus only on individual in-memory data structures. An Android GUI, however, displays diverse visual elements each built from numerous data structure instances. Furthermore, whenever an app is sent to the background, its GUI structure will be explicitly deallocated and disintegrated by the Android framework. In this paper, we present GUITAR, an app-independent technique which automatically reassembles and redraws all apps' GUIs from the multitude of GUI data elements found in a smartphone's memory image. To do so, GUITAR involves the reconstruction of (1) GUI tree topology, (2) drawing operation mapping, and (3) runtime environment for redrawing. Our evaluation shows that GUITAR is highly accurate (80-95% similar to original screenshots) at reconstructing GUIs from memory images taken from a variety of Android apps on popular phones. Moreover, GUITAR is robust in reconstructing meaningful GUIs even when facing GUI data loss.


Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation

Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.


The Spectrum Kernel: A String Kernel for SVM Protein Classification

We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.


Have biopesticides come of age?

Biopesticides based on living microbes and their bioactive compounds have been researched and promoted as replacements for synthetic pesticides for many years. However, lack of efficacy, inconsistent field performance and high cost have generally relegated them to niche products. Recently, technological advances and major changes in the external environment have positively altered the outlook for biopesticides. Significant increases in market penetration have been made, but biopesticides still only make up a small percentage of pest control products. Progress in the areas of activity spectra, delivery options, persistence of effect and implementation have contributed to the increasing use of biopesticides, but technologies that are truly transformational and result in significant uptake are still lacking.


Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications

BACKGROUND
The cytochrome P450 CYP2C9 is responsible for the metabolism of S-warfarin. Two known allelic variants CYP2C9*2 and CYP2C9*3 differ from the wild type CYP2C9*1 by a single aminoacid substitution in each case. The allelic variants are associated with impaired hydroxylation of S-warfarin in in-vitro expression systems. We have studied the effect of CYP2C9 polymorphism on the in-vivo warfarin dose requirement.

METHODS
Patients with a daily warfarin dose requirement of 1.5 mg or less (low-dose group, n=36), randomly selected patients with a wide range of dose requirements from an anticoagulant clinic in north-east England (clinic control group, n=52), and 100 healthy controls from the community in the same region were studied. Genotyping for the CYP2C9*2 and CYP2C9*3 alleles was done by PCR analysis. Case notes were reviewed to assess the difficulties encountered during the induction of warfarin therapy and bleeding complications in the low-dose and clinic control groups.

FINDINGS
The odds ratio for individuals with a low warfarin dose requirement having one or more CYP2C9 variant alleles compared with the normal population was 6.21 (95% CI 2.48-15.6). Patients in the low-dose group were more likely to have difficulties at the time of induction of warfarin therapy (5.97 [2.26-15.82]) and have increased risk of major bleeding complications (rate ratio 3.68 [1.43-9.50]) when compared with randomly selected clinic controls.

INTERPRETATION
We have shown that there is a strong association between CYP2C9 variant alleles and low warfarin dose requirement. CYP2C9 genotyping may identify a subgroup of patients who have difficulty at induction of warfarin therapy and are potentially at a higher risk of bleeding complications.


Ant System

Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this ares has mainly focused on the development of algorithmic variants which achieve better performance than AS. In this article, we present – Ant System, an Ant Colony Optimization algorithm derived from Ant System. – Ant System differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that – Ant System is currently among the best performing algorithms for these problems.


Don't Interrupt Me While I Type: Inferring Text Entered Through Gesture Typing on Android Keyboards

We present a new side-channel attack against soft keyboards that support gesture typing on Android smartphones. An application without any special permissions can observe the number and timing of the screen hardware interrupts and system-wide software interrupts generated during user input, and analyze this information to make inferences about the text being entered by the user. System-wide information is usually considered less sensitive than app-specific information, but we provide concrete evidence that this may be mistaken. Our attack applies to all Android versions, including Android M where the SELinux policy is tightened. We present a novel application of a recurrent neural network as our classifier to infer text. We evaluate our attack against the “Google Keyboard” on Nexus 5 phones and use a real-world chat corpus in all our experiments. Our evaluation considers two scenarios. First, we demonstrate that we can correctly detect a set of pre-defined “sentences of interest” (with at least 6 words) with 70% recall and 60% precision. Second, we identify the authors of a set of anonymous messages posted on a messaging board. We find that even if the messages contain the same number of words, we correctly re-identify the author more than 97% of the time for a set of up to 35 sentences. Our study demonstrates a new way in which systemwide resources can be a threat to user privacy. We investigate the effect of rate limiting as a countermeasure but find that determining a proper rate is error-prone and fails in subtle cases. We conclude that real-time interrupt information should be made inaccessible, perhaps via a tighter SELinux policy in the next Android version.


Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches.

It is well known that when data are nonnormally distributed, a test of the significance of Pearson's r may inflate Type I error rates and reduce power. Statistics textbooks and the simulation literature provide several alternatives to Pearson's correlation. However, the relative performance of these alternatives has been unclear. Two simulation studies were conducted to compare 12 methods, including Pearson, Spearman's rank-order, transformation, and resampling approaches. With most sample sizes (n ≥ 20), Type I and Type II error rates were minimized by transforming the data to a normal shape prior to assessing the Pearson correlation. Among transformation approaches, a general purpose rank-based inverse normal transformation (i.e., transformation to rankit scores) was most beneficial. However, when samples were both small (n ≤ 10) and extremely nonnormal, the permutation test often outperformed other alternatives, including various bootstrap tests.


An Autonomous Multi-UAV System for Search and Rescue

This paper proposes and evaluates a modular architecture of an autonomous unmanned aerial vehicle (UAV) system for search and rescue missions. Multiple multicopters are coordinated using a distributed control system. The system is implemented in the Robot Operating System (ROS) and is capable of providing a real-time video stream from a UAV to one or more base stations using a wireless communications infrastructure. The system supports a heterogeneous set of UAVs and camera sensors. If necessary, an operator can interfere and reduce the autonomy. The system has been tested in an outdoor mission serving as a proof of concept. Some insights from these tests are described in the paper.


Examining the benefits and challenges of using audience response systems: A review of the literature

0360-1315/$ see front matter Crown Copyright 2 doi:10.1016/j.compedu.2009.05.001 * Corresponding author. Tel.: +1 905 721 8668x267 E-mail address: robin.kay@uoit.ca (R.H. Kay). 1 Tel.: +1 905 721 8668x2886. Audience response systems (ARSs) permit students to answer electronically displayed multiple choice questions using a remote control device. All responses are instantly presented, in chart form, then reviewed and discussed by the instructor and the class. A brief history of ARSs is offered including a discussion of the 26 labels used to identify this technology. Next a detailed review of 67 peer-reviewed papers from 2000 to 2007 is offered presenting the benefits and challenges associated with the use of an ARS. Key benefits for using ARSs include improvements to the classroom environment (increases in attendance, attention levels, participation and engagement), learning (interaction, discussion, contingent teaching, quality of learning, learning performance), and assessment (feedback, formative, normative). The biggest challenges for teachers in using ARSs are time needed to learn and set up the ARS technology, creating effective ARS questions, adequate coverage of course material, and ability to respond to instantaneous student feedback. Student challenges include adjusting to a new method of learning, increased confusion when multiple perspectives are discussed, and negative reactions to being monitored. It is concluded that more systematic, detailed research is needed in a broader range of contexts. Crown Copyright 2009 Published by Elsevier Ltd. All rights reserved.


Updating freeze: Aligning animal and human research

Freezing is widely used as the main outcome measure for fear in animal studies. Freezing is also getting attention more frequently in human stress research, as it is considered to play an important role in the development of psychopathology. Human models on defense behavior are largely based on animal models. Unfortunately, direct translations between animal and human studies are hampered by differences in definitions and methods. The present review therefore aims to clarify the conceptualization of freezing. Neurophysiological and neuroanatomical correlates are discussed and a translational model is proposed. We review the upcoming research on freezing in humans that aims to match animal studies by using physiological indicators of freezing (bradycardia and objective reduction in movement). Finally, we set the agenda for future research in order to optimize mutual animal-human translations and stimulate consistency and systematization in future empirical research on the freezing phenomenon.


Child abuse, neglect, and adult behavior: research design and findings on criminality, violence, and child abuse.

Using a prospective cohorts design, a large sample of physical and sexual abuse cases was compared to a matched control group. Overall, abused and neglected subjects had higher rates than did controls for adult criminality and arrests for violent offenses, but not for adult arrests for child abuse or neglect. Findings are discussed in the context of intergenerational transmission of violence, and directions for future research are suggested.


An approach for selecting seed URLs of focused crawler based on user-interest ontology

Seed URLs selection for focused Web crawler intends to guide related and valuable information that meets a user’s personal information requirement and provide more effective information retrieval. In this paper, we propose a seed URLs selection approach based on user-interest ontology. In order to enrich semantic query, we first intend to apply Formal Concept Analysis to construct user-interest concept lattice with user log profile. By using concept lattice merger, we construct the user-interest ontology which can describe the implicit concepts and relationships between them more appropriately for semantic representation eed URLs ormal concept analysis ser-interest ontology ipartite graph eb crawler and query match. On the other hand, we make full use of the user-interest ontology for extracting the user interest topic area and expanding user queries to receive the most related pages as seed URLs, which is an entrance of the focused crawler. In particular, we focus on how to refine the user topic area using the bipartite directed graph. The experiment proves that the user-interest ontology can be achieved effectively by merging concept lattices and that our proposed approach can select high quality seed URLs collection and improve the average precision of focused Web crawler.


Compact arrays fed by substrate integrated waveguides

In this paper, we compare three different concepts of compact antenna arrays fed by substrate integrated waveguides (SIW). Antenna concepts differ in the type of radiators. Slots represent magnetic linear radiators, patches are electric surface radiators, and Vivaldi slots belong to travelling-wave antennas. Hence, the SIW feeders have to exploit different mechanisms of exciting antenna elements. Impedance and radiation properties of studied antenna arrays have been related to the normalized frequency. Antenna arrays have been mutually compared to show fundamental dependencies of final parameters of the designed antennas on state variables of antennas, on SIW feeder architectures and on related implementation details.


SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE

Seaport monitoring and management is a significant research area, in which infrastructure automatically collects big data sets that lead the organization in its multiple activities. Thus, this problem is heavily related to the fields of data acquisition, transfer, storage, big data analysis and information visualization. Las Palmas de Gran Canaria port is a good example of how a seaport generates big data volumes through a network of sensors. They are placed on meteorological stations and maritime buoys, registering environmental parameters. Likewise, the Automatic Identification System (AIS) registers several dynamic parameters about the tracked vessels. However, such an amount of data is useless without a system that enables a meaningful visualization and helps make decisions. In this work, we present SmartPort, a platform that offers a distributed architecture for the collection of the port sensors' data and a rich Internet application that allows the user to explore the geolocated data. The presented SmartPort tool is a representative, promising and inspiring approach to manage and develop a smart system. It covers a demanding need for big data analysis and visualization utilities for managing complex infrastructures, such as a seaport.


Modeling textual entailment with role semantic information

In this thesis, we present a novel approach for modeling textual entailment using lexicalsemantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution of frame semantic information to the recognition textual entailment and (ii) to present a robust system architecture that can serve as basis for future experiments, research, and improvement. Our work was carried out in the context of the textual entailment initiative, which since 2005 has set the stage for the broad investigation of inference in natural-language processing tasks, including empirical evaluation of its coverage and reliability. In short, textual entailment describes inferential relations between (entailing) texts and (entailed) hypotheses as interpreted by typical language users. This pre-theoretic notion captures a natural range of inferences as compared to logical entailment, which has traditionally been used within theoretical approaches to natural language semantics. Various methods for modeling textual entailment have been proposed in the literature, ranging from shallow techniques like lexical overlap to shallow syntactic parsing and the exploitation of WordNet relations. Recently, there has been a move towards more structured meaning representations. In particular, the level of predicate-argument structure has gained much attention, which seems to be a natural and straightforward choice. Predicate-argument structure allows annotating sentences or texts with nuclear meaning representations (“who did what to whom”), which are of obvious relevance for this task. For example, it can account for paraphrases like “Ghosts scare John” vs. “John is scared by ghosts”. In this thesis, we present an approach to textual entailment that is centered around the analysis of predicate-argument structure. It combines LFG grammatical analysis, predicate-argument structure in the FrameNet paradigm, and taxonomic information from WordNet into tripartite graph structures. By way of a declarative graph matching algorithm, the “structural and semantic” similarity of hypotheses and texts is computed and the result is represented as feature vectors. A supervised machine learning architecture trained on entailment corpora is used to check textual entailment for new text/hypothesis pairs. The approach is implemented in the SALSA RTE system, which successfully participated in the second and third RTE challenge. While system performance is on a par with that of comparable systems, the intuitively expected strong positive effect of using FrameNet information has not yet been confirmed. In order to evaluate different system components and to assess the potential contribution of FrameNet information for checking textual entailment, we conducted a number of experiments. For example, with the help of a gold-standard corpus, we


An algorithm for treatment of the drooping nose.

UNLABELLED
Nasal tip ptosis ("drooping" or long nose) occurs when the tip of the nose is more caudal than what is deemed ideal. Intrinsic factors, such as elongated or caudally-rotated lower lateral cartilages, can lead to nasal tip ptosis. Extrinsic factors, such as elongated upper lateral cartilages or excessive caudal anterior septum and heavy nasal skin, can push the nasal tip caudally and lead to drooping of the nasal tip. The loss of maxillary or nasal spine support may enhance the potential for tip ptosis. In addition, a hyperactive depressor nasi septi could, as a result of continuous pull on the tip, result in tip ptosis. Aging or previous nasal procedures (such as the Goldman-type tip surgery) where the continuity of the lateral and medial crura of the lower lateral cartilages have been violated may cause a weakening of the tip-supporting mechanisms and de-rotation of the nasal tip. Correction of this deformity is challenging and rewarding; it can resolve both the cosmetic deformity and nasal obstruction symptoms related to this entity. The goal of this article is to present our current principles of diagnosis and treatment of nasal tip ptosis, as well as to introduce and algorithm of preferred methods and techniques for its reliable and stable correction.

RESULTS
Correction of the nasal tip ptosis requires accurate diagnosis, a recognition of the interplay between various anatomic components, specific strategy planning, and a correction of anatomic abnormalities.


Emerging
Applications of Liquid Metals Featuring Surface Oxides

Gallium and several of its alloys are liquid metals at or near room temperature. Gallium has low toxicity, essentially no vapor pressure, and a low viscosity. Despite these desirable properties, applications calling for liquid metal often use toxic mercury because gallium forms a thin oxide layer on its surface. The oxide interferes with electrochemical measurements, alters the physicochemical properties of the surface, and changes the fluid dynamic behavior of the metal in a way that has, until recently, been considered a nuisance. Here, we show that this solid oxide "skin" enables many new applications for liquid metals including soft electrodes and sensors, functional microcomponents for microfluidic devices, self-healing circuits, shape-reconfigurable conductors, and stretchable antennas, wires, and interconnects.


A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile

The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP). The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile. We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs. Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results.


A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initialized with embeddings that make use of character Ngram information to better suit this task. When evaluated on common benchmark test data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior neural approaches on this task as well as strong statistical machine translation-based systems with neural and task-specific features trained on the same data. Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory (LSTM) networks in capturing the local context via attention, and thereby improving the coverage in correcting grammatical errors. By ensembling multiple models, and incorporating an N-gram language model and edit features via rescoring, our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach, both in terms of grammaticality and fluency.


A self-steering I/Q receiver array in 45-nm CMOS SOI

A novel I/Q receiver array is demonstrated that adapts phase shifts in each receive channel to point a receive beam toward an incident RF signal. The measured array operates at 8.1 GHz and covers steering angles of +/-35 degrees for a four element array. Additionally, the receiver incorporates an I/Q down-converter and demodulates 64QAM with EVM less than 4%. The chip is fabricated in 45 nm CMOS SOI process and occupies an area of 3.45 mm2 while consuming 143 mW dc power.


A Technical Approach to the Energy Blockchain in Microgrids

The present paper considers some technical issues related to the “energy blockchain” paradigm applied to microgrids. In particular, what appears from the study is that the superposition of energy transactions in a microgrid creates a variation of the power losses in all the branches of the microgrid. Traditional power losses allocation in distribution systems takes into account only generators while, in this paper, a real-time attribution of power losses to each transaction involving one generator and one load node is done by defining some suitable indices. Besides, the presence of P–V nodes increases the level of reactive flows and provides a more complex technical perspective. For this reason, reactive power generation for voltage support at P–V nodes poses a further problem of reactive power flow exchange, which is worth of investigation in future works in order to define a possible way of remuneration. The experimental section of the paper considers a medium voltage microgrid and two different operational scenarios.


Aspect extraction in sentiment analysis: comparative analysis and survey

Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user’s sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.


An incremental deployment algorithm for mobile robot teams

This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots’ sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another. This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated


RABAC: Role-Centric Attribute-Based Access Control

Role-based access control (RBAC) is a commercially dominant model, standardized by the National Institute of Standards and Technology (NIST). Although RBAC provides compelling benefits for security management it has several known deficiencies such as role explosion, wherein multiple closely related roles are required (e.g., attendingdoctor role is separately defined for each patient). Numerous extensions to RBAC have been proposed to overcome these shortcomings. Recently NIST announced an initiative to unify and standardize these extensions by integrating roles with attributes, and identified three approaches: use attributes to dynamically assign users to roles, treat roles as just another attribute, and constrain the permissions of a role via attributes. The first two approaches have been previously studied. This paper presents a formal model for the third approach for the first time in the literature. We propose the novel role-centric attribute-based access control (RABAC) model which extends the NIST RBAC model with permission filtering policies. Unlike prior proposals addressing the role-explosion problem, RABAC does not fundamentally modify the role concept and integrates seamlessly with the NIST RBAC model. We also define an XACML profile for RABAC based on the existing XACML profile for RBAC.


A throwable miniature robotic system

Before the soldiers or police take actions, a fast and real-time detection in dangerous places, such as indoor, passageway and underpass, can both guarantee the soldiers' safety and increase the battle's accuracy, so it is significative and necessary to design a kind of scout robot which is able to arrive in the target region to acquire the message fast by the ways of thrown, shot or airdrop. This kind of robot should be small, easy to hide and have the ability of anti-impact and semi-autonomous. This paper mainly proposes a design method of the throwable miniature scout robot, analyses the anti-pact mechanism and autonomous strategy, and shows that the anti-impact mechanism is useful to attenuate the impact and the semi-autonomous control strategy can be fit for the robot's application through several experiments at last.


Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex.

To make economic choices between goods, the brain needs to compute representations of their values. A great deal of research has been performed to determine the neural correlates of value representations in the human brain. However, it is still unknown whether there exists a region of the brain that commonly encodes decision values for different types of goods, or if, in contrast, the values of different types of goods are represented in distinct brain regions. We addressed this question by scanning subjects with functional magnetic resonance imaging while they made real purchasing decisions among different categories of goods (food, nonfood consumables, and monetary gambles). We found activity in a key brain region previously implicated in encoding goal-values: the ventromedial prefrontal cortex (vmPFC) was correlated with the subjects' value for each category of good. Moreover, we found a single area in vmPFC to be correlated with the subjects' valuations for all categories of goods. Our results provide evidence that the brain encodes a "common currency" that allows for a shared valuation for different categories of goods.


Sentiment Identification in Code-Mixed Social Media Text

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying presence of sentiment in text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is presence of sentiment in text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach. While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labelling Facebook posts with their associated sentiments.


Social signal processing: Survey of an emerging domain

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.


Hipster Wars: Discovering Elements of Fashion Styles

The clothing we wear and our identities are closely tied, revealing to the world clues about our wealth, occupation, and socioidentity. In this paper we examine questions related to what our clothing reveals about our personal style. We first design an online competitive Style Rating Game called Hipster Wars to crowd source reliable human judgments of style. We use this game to collect a new dataset of clothing outfits with associated style ratings for 5 style categories: hipster, bohemian, pinup, preppy, and goth. Next, we train models for betweenclass and within-class classification of styles. Finally, we explore methods to identify clothing elements that are generally discriminative for a style, and methods for identifying items in a particular outfit that may indicate a style.


Where to Buy It: Matching Street Clothing Photos in Online Shops

In this paper, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same item in an online shop. This is an extremely challenging task due to visual differences between street photos (pictures of people wearing clothing in everyday uncontrolled settings) and online shop photos (pictures of clothing items on people, mannequins, or in isolation, captured by professionals in more controlled settings). We collect a new dataset for this application containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos. We develop three different methods for Exact Street to Shop retrieval, including two deep learning baseline methods, and a method to learn a similarity measure between the street and shop domains. Experiments demonstrate that our learned similarity significantly outperforms our baselines that use existing deep learning based representations.


Style Finder: Fine-Grained Clothing Style Detection and Retrieval

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.


Matching-CNN meets KNN: Quasi-parametric human parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN [12] in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts [29, 30], for the human parsing task.


The SeaLion has Landed: An IDE for Answer-Set Programming---Preliminary Report

We report about the current state and designated features of the tool SeaLion, aimed to serve as an integrated development environment (IDE) for answer-set programming (ASP). A main goal of SeaLion is to provide a user-friendly environment for supporting a developer to write, evaluate, debug, and test answer-set programs. To this end, new support techniques have to be developed that suit the requirements of the answer-set semantics and meet the constraints of practical applicability. In this respect, SeaLion benefits from the research results of a project on methods and methodologies for answer-set program development in whose context SeaLion is realised. Currently, the tool provides source-code editors for the languages of Gringo and DLV that offer syntax highlighting, syntax checking, and a visual program outline. Further implemented features are support for external solvers and visualisation as well as visual editing of answer sets. SeaLion comes as a plugin of the popular Eclipse platform and provides itself interfaces for future extensions of the IDE.


Occlusion horizons for driving through urban scenery

Wepresentarapidocclusioncullingalgorithmspecificallydesigned for urbanenvironments. For eachframe,an occlusionhorizonis beingbuilt on-the-flyduring a hierarchicalfront-to-backtraversal of the scene.All conservatively hiddenobjectsareculled, while all theoccludingimpostorsof all conservatively visibleobjectsare addedto the  D occlusionhorizon.Our framework alsosupports levels-of-detail(LOD) renderingby estimatingthe visible areaof the projectionof an objectin orderto selecttheappropriateLOD for eachobject.This algorithmrequiresno substantial preprocessing andnoexcessi ve storage. In a test sceneof 10,000buildings, the cull phasetook 11 ms on a PentiumII333 MHz and45 ms on an SGI Octaneper frame on average. In typical views, the occlusionhorizonculled away 80-90%of theobjectsthatwerewithin theview frustum,giving a 10 timesspeedupover view frustumculling alone.Combiningthe occlusionhorizonwith LOD renderinggavea17 timesspeedupon anSGIOctane,and23 timeson aPII. CR Categoriesand SubjectDescriptors: I.3.7[ComputerGraphics]: I.3.7 Three-Dimensional GraphicsandRealism.


Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding

One of the key problems in spoken language understanding (SLU) is the task of slot filling. In light of the recent success of applying deep neural network technologies in domain detection and intent identification, we carried out an in-depth investigation on the use of recurrent neural networks for the more difficult task of slot filling involving sequence discrimination. In this work, we implemented and compared several important recurrent-neural-network architectures, including the Elman-type and Jordan-type recurrent networks and their variants. To make the results easy to reproduce and compare, we implemented these networks on the common Theano neural network toolkit, and evaluated them on the ATIS benchmark. We also compared our results to a conditional random fields (CRF) baseline. Our results show that on this task, both types of recurrent networks outperform the CRF baseline substantially, and a bi-directional Jordantype network that takes into account both past and future dependencies among slots works best, outperforming a CRFbased baseline by 14% in relative error reduction.


Accessibility Evaluation of Classroom Captions

Real-time captioning enables deaf and hard of hearing (DHH) people to follow classroom lectures and other aural speech by converting it into visual text with less than a five second delay. Keeping the delay short allows end-users to follow and participate in conversations. This article focuses on the fundamental problem that makes real-time captioning difficult: sequential keyboard typing is much slower than speaking. We first surveyed the audio characteristics of 240 one-hour-long captioned lectures on YouTube, such as speed and duration of speaking bursts. We then analyzed how these characteristics impact caption generation and readability, considering specifically our human-powered collaborative captioning approach. We note that most of these characteristics are also present in more general domains. For our caption comparison evaluation, we transcribed a classroom lecture in real-time using all three captioning approaches. We recruited 48 participants (24 DHH) to watch these classroom transcripts in an eye-tracking laboratory. We presented these captions in a randomized, balanced order. We show that both hearing and DHH participants preferred and followed collaborative captions better than those generated by automatic speech recognition (ASR) or professionals due to the more consistent flow of the resulting captions. These results show the potential to reliably capture speech even during sudden bursts of speed, as well as for generating “enhanced” captions, unlike other human-powered captioning approaches.


A Decentralised Sharing App running a Smart Contract on the Ethereum Blockchain

The sharing economy, the business of collectively using privately owned objects and services, has fuelled some of the fastest growing businesses of the past years. However, popular sharing platforms like Airbnb or Uber exhibit several drawbacks: a cumbersome sign up procedure, lack of participant privacy, overbearing terms and conditions, and significant fees for users. We demonstrate a Decentralised App (DAPP) for the sharing of everyday objects based on a smart contract on the Ethereum blockchain. This contract enables users to register and rent devices without involvement of a Trusted Third Party (TTP), disclosure of any personal information or prior sign up to the service. With increasing distribution of cryptocurrencies the use of smart contracts such as proposed in this paper has the potential to revolutionise the sharing economy.


Layout-sensitive language extensibility with SugarHaskell

Programmers need convenient syntax to write elegant and concise programs. Consequently, the Haskell standard provides syntactic sugar for some scenarios (e.g., do notation for monadic code), authors of Haskell compilers provide syntactic sugar for more scenarios (e.g., arrow notation in GHC), and some Haskell programmers implement preprocessors for their individual needs (e.g., idiom brackets in SHE). But manually written preprocessors cannot scale: They are expensive, error-prone, and not composable. Most researchers and programmers therefore refrain from using the syntactic notations they need in actual Haskell programs, but only use them in documentation or papers. We present a syntactically extensible version of Haskell, SugarHaskell, that empowers ordinary programmers to implement and use custom syntactic sugar.
 Building on our previous work on syntactic extensibility for Java, SugarHaskell integrates syntactic extensions as sugar libraries into Haskell's module system. Syntax extensions in SugarHaskell can declare arbitrary context-free and layout-sensitive syntax. SugarHaskell modules are compiled into Haskell modules and further processed by a Haskell compiler. We provide an Eclipse-based IDE for SugarHaskell that is extensible, too, and automatically provides syntax coloring for all syntax extensions imported into a module.
 We have validated SugarHaskell with several case studies, including arrow notation (as implemented in GHC) and EBNF as a concise syntax for the declaration of algebraic data types with associated concrete syntax. EBNF declarations also show how to extend the extension mechanism itself: They introduce syntactic sugar for using the declared concrete syntax in other SugarHaskell modules.


Theodor Lipps and the shift from "sympathy" to "empathy".

In the course of extensive philosophical debates on aesthetics in nineteenth-century Germany, Robert Vischer introduced the concept of Einfühlung in relation to art. Theodor Lipps subsequently extended it from art to visual illusions and interpersonal understanding. While Lipps had regarded Einfühlung as basically similar to the old notion of sympathy, Edward Titchener in America believed it had a different meaning. Hence, he coined the term empathy as its translation. This term came to be increasingly widely adopted, first in psychology and then more generally. But the lack of agreement about the supposed difference between these concepts suggests that Lipps had probably been right.


A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics

Metrics for structured data have received an increasing interest in the machine learning community. Graphs provide a natural representation for structured data, but a lot of operations on graphs are computationally intractable. In this article, we present a polynomial-time algorithm that computes a maximum common subgraph of two outerplanar graphs. The algorithm makes use of the block-and-bridge preserving subgraph isomorphism, which has significant efficiency benefits and is also motivated from a chemical perspective. We focus on the application of learning structure-activity relationships, where the task is to predict the chemical activity of molecules. We show how the algorithm can be used to construct a metric for structured data and we evaluate this metric and more generally also the block-and-bridge preserving matching operator on 60 molecular datasets, obtaining state-of-the-art results in terms of predictive performance and efficiency.


Dynamic Integration of Background Knowledge in Neural NLU Systems

Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the taskspecific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.


Preserving Relation Privacy in Online Social Network Data

Online social networks routinely publish data of interest to third parties, but in so doing often reveal relationships, such as a friendship or contractual association, that an attacker can exploit. This systematic look at existing privacy-preservation techniques highlights the vulnerabilities of users even in networks that completely anonymize identities. Through a taxonomy that categorizes techniques according to the degree of user identity exposure, the authors examine the ways that existing approaches compromise relation privacy and offer more secure alternatives.


Emotion and the motivational brain

Psychophysiological and neuroscience studies of emotional processing undertaken by investigators at the University of Florida Laboratory of the Center for the Study of Emotion and Attention (CSEA) are reviewed, with a focus on reflex reactions, neural structures and functional circuits that mediate emotional expression. The theoretical view shared among the investigators is that expressed emotions are founded on motivational circuits in the brain that developed early in evolutionary history to ensure the survival of individuals and their progeny. These circuits react to appetitive and aversive environmental and memorial cues, mediating appetitive and defensive reflexes that tune sensory systems and mobilize the organism for action and underly negative and positive affects. The research reviewed here assesses the reflex physiology of emotion, both autonomic and somatic, studying affects evoked in picture perception, memory imagery, and in the context of tangible reward and punishment, and using the electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), explores the brain's motivational circuits that determine human emotion.


Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.


Learning Inference Models for Computer Vision

Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call ‘Bilateral Neural Networks’. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. Experiments demonstrate the potential of the proposed bilateral networks on a wide range of vision tasks and datasets. In summary, we propose learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks. In generative vision models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way for incorporating prior knowledge into CNNs.


Tunneling Transistors Based on Graphene and 2-D Crystals

As conventional transistors become smaller and thinner in the quest for higher performance, a number of hurdles are encountered. The discovery of electronic-grade 2-D crystals has added a new “layer” to the list of conventional semiconductors used for transistors. This paper discusses the properties of 2-D crystals by comparing them with their 3-D counterparts. Their suitability for electronic devices is discussed. In particular, the use of graphene and other 2-D crystals for interband tunneling transistors is discussed for low-power logic applications. Since tunneling phenomenon in reduced dimensions is not conventionally covered in texts, the physics is developed explicitly before applying it to transistors. Though we are in an early stage of learning to design devices with 2-D crystals, they have already been the motivation behind a list of truly novel ideas. This paper reviews a number of such ideas.


Autonomous reinforcement learning on raw visual input data in a real world application

We propose a learning architecture, that is able to do reinforcement learning based on raw visual input data. In contrast to previous approaches, not only the control policy is learned. In order to be successful, the system must also autonomously learn, how to extract relevant information out of a high-dimensional stream of input information, for which the semantics are not provided to the learning system. We give a first proof-of-concept of this novel learning architecture on a challenging benchmark, namely visual control of a racing slot car. The resulting policy, learned only by success or failure, is hardly beaten by an experienced human player.


Capturing and animating skin deformation in human motion

During dynamic activities, the surface of the human body moves in many subtle but visually significant ways: bending, bulging, jiggling, and stretching. We present a technique for capturing and animating those motions using a commercial motion capture system and approximately 350 markers. Although the number of markers is significantly larger than that used in conventional motion capture, it is only a sparse representation of the true shape of the body. We supplement this sparse sample with a detailed, actor-specific surface model. The motion of the skin can then be computed by segmenting the markers into the motion of a set of rigid parts and a residual deformation (approximated first as a quadratic transformation and then with radial basis functions). We demonstrate the power of this approach by capturing flexing muscles, high frequency motions, and abrupt decelerations on several actors. We compare these results both to conventional motion capture and skinning and to synchronized video of the actors.


Ontology and the Lexicon

A lexicon is a linguistic object and hence is not the same thing as an ontology, which is non-linguistic. Nonetheless, word senses are in many ways similar to ontological concepts and the relationships found between word senses resemble the relationships found between concepts. Although the arbitrary and semi-arbitrary distinctions made by natural languages limit the degree to which these similarities can be exploited, a lexicon can nonetheless serve in the development of an ontology, especially in a technical domain.


A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss

We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-theart ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.


The TangibleK Robotics Program: Applied Computational Thinking for Young Children.

This article describes the TangibleK robotics program for young children. Based on over a decade of research, this program is grounded on the belief that teaching children about the human-made world, the realm of technology and engineering, is as important as teaching them about the natural world, numbers, and letters. The TangibleK program uses robotics as a tool to engage children in developing computational thinking and learning about the engineering design process. It includes a research-based, developmentally appropriate robotics kit that children can use to make robots and program their behaviors. The curriculum has been piloted in kindergarten classrooms and in summer camps and lab settings. The author presents the theoretical framework that informs TangibleK and the “powerful ideas” from computer science and robotics on which the curriculum is based, linking them to other disciplinary areas and developmental characteristics of early childhood. The article concludes with a description of classroom pedagogy and activities, along with assessment tools used to evaluate learning outcomes.


Associations between young adults' use of sexually explicit materials and their sexual preferences, behaviors, and satisfaction.

This study examined how levels of sexually explicit material (SEM) use during adolescence and young adulthood were associated with sexual preferences, sexual behaviors, and sexual and relationship satisfaction. Participants included 782 heterosexual college students (326 men and 456 women; M(age) = 19.9) who completed a questionnaire online. Results revealed high frequencies and multiple types and contexts of SEM use, with men's usage rates systematically higher than women's. Regression analyses revealed that both the frequency of SEM use and number of SEM types viewed were uniquely associated with more sexual experience (a higher number of overall and casual sexual intercourse partners, as well as a lower age at first intercourse). Higher frequencies of SEM use were associated with less sexual and relationship satisfaction. The frequency of SEM use and number of SEM types viewed were both associated with higher sexual preferences for the types of sexual practices typically presented in SEM. These findings suggest that SEM use can play a significant role in a variety of aspects of young adults' sexual development processes.


Improved Collision Attack on Hash Function MD5

In this paper, we present a fast attack algorithm to find two-block collision of hash function MD5. The algorithm is based on the two-block collision differential path of MD5 that was presented by Wang et al. in the Conference EUROCRYPT 2005. We found that the derived conditions for the desired collision differential path were not sufficient to guarantee the path to hold and that some conditions could be modified to enlarge the collision set. By using technique of small range searching and omitting the computing steps to check the characteristics in the attack algorithm, we can speed up the attack of MD5 efficiently. Compared with the Advanced Message Modification technique presented by Wang et al., the small range searching technique can correct 4 more conditions for the first iteration differential and 3 more conditions for the second iteration differential, thus improving the probability and the complexity to find collisions. The whole attack on the MD5 can be accomplished within 5 hours using a PC with Pentium4 1.70GHz CPU.


Complementary approaches built as web service for arabic handwriting OCR systems via amazon elastic mapreduce (EMR) model

Arabic Optical Character Recognition (OCR) as Web Services represents a major challenge for handwritten document recognition. A variety of approaches, methods, algorithms and techniques have been proposed in order to build powerful Arabic OCR web services. Unfortunately, these methods could not succeed in achieving this mission in case of large large quantity Arabic handwritten documents. Intensive experiments and observations revealed that some of the existing approaches and techniques are complementary and can be combined to improve the recognition rate. Designing and implementing these recent sophisticated complementary approaches and techniques as web services are commonly complex; they require strong computing power to reach an acceptable recognition speed especially in case of large quantity documents. One of the possible solutions to overcome this problem is to benefit from distributed computing architectures such as cloud computing. This paper describes the design and implementation of Arabic Handwriting Recognition as a web service (AHRweb service) based on the complementary approach K-Nearest Neighbor (KNN) /Support Vector Machine (SVM) (K-NN/SVM) via Amazon Elastic MapReduce (EMR) model. The experiments were conducted on a cloud computing environment with a real large scale handwriting dataset from the Institute for Communications Technology (IFN)/ Ecole Nationale d’Ingénieur de Tunis (ENIT) IFN/ENIT database. The J-Sim (Java Simulator) was used as a tool to generate and analyze statistical results. Experimental results show that Amazon Elastic MapReduce (EMR) model constitutes a very promising framework for enhancing large AHRweb service performances.


Improving returns on stock investment through neural network selection

Artificial neural networks’ (ANNs’) generalization powers have in recent years received admiration of finance researchers and practitioners. Their usage in such areas as bankruptcy prediction, debt-risk assessment, and security-market applications has yielded promising results. With such intensive research and proven ability of the ANN in the area of security-market application and the growing importance of the role of equity securities in Singapore, it has motivated the conceptual development of this work in using the ANN in stock selection. With their proven generalization ability, neural networks are able to infer the characteristics of performing stocks from the historical patterns. The performance of stocks is reflective of the profitability and quality of management of the underlying company. Such information is reflected in financial and technical variables. As such, the ANN is used as a tool to uncover the intricate relationships between the performance of stocks and the related financial and technical variables. Historical data, such as financial variables (inputs) and performance of the stock (output) is used in this ANN application. Experimental results obtained thus far have been very encouraging. IDEA GROUP PUBLISHING This paper appears in the publication, Artificial Neural Networks in Finance and Manufacturing edited by Joarder Kamruzzaman, Rezaul Begg, and Ruhul Sarker© 2006, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB13014 Improving Returns on Stock Investment through Neural Network Selection 153 Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Introduction With the growing importance in the role of equities to both international and local investors, the selection of attractive stocks is of utmost importance to ensure a good return. Therefore, a reliable tool in the selection process can be of great assistance to these investors. An effective and efficient tool/system gives the investor the competitive edge over others as he/she can identify the performing stocks with minimum effort. In assisting the investors in their decision-making process, both the academics and practitioners have devised trading strategies, rules, and concepts based on fundamental and technical analysis. Innovative investors opt to employ information technology to improve the efficiency in the process. This is done through transforming trading strategies into computer-known language so as to exploit the logical processing power of the computer. This greatly reduces the time and effort in short-listing the list of attractive stocks. In the age where information technology is dominant, such computerized rule-based expert systems have severe limitations that will affect their effectiveness and efficiency. In particular, their inability in handling nonlinear relationships between financial variables and stock prices has been a major shortcoming. However, with the significant advancement in the field of ANNs, these limitations have found a solution. In this work, the generalization ability of the ANN is being harnessed in creating an effective and efficient tool for stock selection. Results of the research in this field have so far been very encouraging. Application of Neural Network in Stock Investment One of the earliest studies was by Halquist and Schmoll (1989), who used a neural network model to predict trends in the S&P 500 index. They found that the model was able to predict the trends 61% of the time. This was followed by Trippi and DeSieno (1992) and Grudnitski and Osburn (1993). Trippi and DeSieno (1992) devised an S&P 500 trading system that consisted of several trained neural networks and a set of rules for combining the network results to generate a composite recommended trading strategy. The trading system was used to predict S&P 500 index futures and the results showed that this system significantly outperformed the passive buy-and-hold strategy. Grudnitski and Osburn (1993) used a neural network to predict the monthly price changes and trading return in the S&P 500 index futures. The results showed that the neural network was able to predict correctly 75% of the time and gave a positive return above risk. Another work on predicting S&P 500 index futures was by Tsaih, Hsu, and Lai (1998). Similar to Trippi and DeSieno (1992), Tsaih et al. (1998) also integrated a rule-based system technique with a neural network to produce a trading system. However, in the Tsaih et al. (1998) study, they used reasoning neural networks instead of the backpropagation method used by Trippi and Desieno (1992). Empirical results in the daily prediction of price changes in the S&P 500 index futures showed that this hybrid artificialintelligence (AI) approach outperformed the passive buy-and-hold investment strategy. 11 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/improving-returns-stockinvestment-through/5354


Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control

In this paper, we treat nonlinear active noise control (NANC) with a linear secondary path (LSP) and with a nonlinear secondary path (NSP) in a unified structure by introducing a new virtual secondary path filter concept and using a general function expansion nonlinear filter. We discover that using the filtered-error structure results in greatly reducing the computational complexity of NANC. As a result, we extend the available filtered-error-based algorithms to solve NANC/LSP problems and, furthermore, develop our adjoint filtered-error-based algorithms for NANC/NSP. This family of algorithms is computationally efficient and possesses a simple structure. We also find that the computational complexity of NANC/NSP can be reduced even more using block-oriented nonlinear models, such as the Wiener, Hammerstein, or linear-nonlinear-linear (LNL) models for the NSP. Finally, we use the statistical properties of the virtual secondary path and the robustness of our proposed methods to further reduce the computational complexity and simplify the implementation structure of NANC/NSP when the NSP satisfies certain conditions. Computational complexity and simulation results are given to confirm the efficiency and effectiveness of all of our proposed methods


Routing perturbation for enhanced security in split manufacturing

Split manufacturing can mitigate security vulnerabilities at untrusted foundries by exposing only partial designs. Even so, attackers can make educated guess according to design conventions and thereby recover entire chip designs. In this work, a routing perturbation-based defense method is proposed such that such attacks become very difficult while wirelength/timing overhead is restricted to be very small. Experimental results on benchmark circuits confirm the effectiveness of the proposed techniques. The new techniques also significantly outperform the latest previous work.


Music emotion classification: a fuzzy approach

Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each music segment, the approach determines how likely the song segment belongs to an emotion class. Two fuzzy classifiers are adopted to provide the measurement of the emotion strength. The measurement is also found useful for tracking the variation of music emotions in a song. Results are shown to illustrate the effectiveness of the approach.


Automatic mood detection and tracking of music audio signals

Music mood describes the inherent emotional expression of a music clip. It is helpful in music understanding, music retrieval, and some other music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. The hierarchical framework has the advantage of emphasizing the most suitable features in different detection tasks. Three feature sets, including intensity, timbre, and rhythm are extracted to represent the characteristics of a music clip. The intensity feature set is represented by the energy in each subband, the timbre feature set is composed of the spectral shape features and spectral contrast features, and the rhythm feature set indicates three aspects that are closely related with an individual's mood response, including rhythm strength, rhythm regularity, and tempo. Furthermore, since mood is usually changeable in an entire piece of classical music, the approach to mood detection is extended to mood tracking for a music piece, by dividing the music into several independent segments, each of which contains a homogeneous emotional expression. Preliminary evaluations indicate that the proposed algorithms produce satisfactory results. On our testing database composed of 800 representative music clips, the average accuracy of mood detection achieves up to 86.3%. We can also on average recall 84.1% of the mood boundaries from nine testing music pieces.


Psychological testing and assessment in the 21st century.

As spin-offs of the current revolution in the cognitive and neurosciences, clinical neuropsychologists in the 21st century will be using biological tests of intelligence and cognition that record individual differences in brain functions at the neuromolecular, neurophysiologic, and neurochemical levels. Assessment of patients will focus more on better use of still intact functions, as well as rehabilitating or bypassing impaired functions, than on diagnosis, as is the focus today. Better developed successors to today's scales for assessing personal competency and adaptive behavior, as well as overall quality of life, also will be in wide use in clinical settings. With more normal individuals, use of new generations of paper-and-pencil inventories, as well as biological measures for assessing differences in interests, attitudes, personality styles, and predispositions, is predicted.


Musical genre classification of audio signals

Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.


A fuzzy K-nearest neighbor algorithm

Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of classification. The K-nearest neighbor decision rule has often been used in these pattern recognition problems. One of the difficulties that arises when utilizing this technique is that each of the labeled samples is given equal importance in deciding the class memberships of the pattern to be classified, regardless of their `typicalness'. The theory of fuzzy sets is introduced into the K-nearest neighbor technique to develop a fuzzy version of the algorithm. Three methods of assigning fuzzy memberships to the labeled samples are proposed, and experimental results and comparisons to the crisp version are presented.


Statistical Learning Algorithms Applied to Automobile Insurance

We recently conducted a research project for a large North American automobile insurer. This study was the most exhaustive ever undertaken by this particular insurer and lasted over an entire year. We analyzed the discriminating power of each variable used for ratemaking. We analyzed the performance of several models within five broad categories: linear regressions, generalized linear models, decision trees, neural networks and support vector machines. In this paper, we present the main results of this study. We qualitatively compare models and show how neural networks can represent high-order nonlinear dependencies with a small number of parameters, each of which is estimated on a large proportion of the data, thus yielding low variance. We thoroughly explain the purpose of the nonlinear sigmoidal transforms which are at the very heart of neural networks’ performances. The main numerical result is a statistically significant reduction in the out-of-sample meansquared error using the neural network model and our ability to substantially reduce the median premium by charging more to the highest risks. This in turn can translate into substantial savings and financial benefits for an insurer. We hope this paper goes a long way in convincing actuaries to include neural networks within their set of modeling tools for ratemaking.


Knowledge sharing in open source software communities: motivations and management

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Hybris: Robust Hybrid Cloud Storage

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources.
 Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f + 1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f + 1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds.
 We implemented Hybris in Java and evaluated it using a series of micro and macrobenchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.


JCLEC: a Java framework for evolutionary computation

In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.


Developing Competency Models to Promote Integrated Human Resource Practices • 309 DEVELOPING COMPETENCY MODELS TO PROMOTE INTEGRATED HUMAN RESOURCE PRACTICES

Today, competencies are used in many facets of human resource management, ranging from individual selection, development, and performance management to organizational strategic planning. By incorporating competencies into job analysis methodologies, the Office of Personnel Management (OPM) has developed robust competency models that can form the foundation for each of these initiatives. OPM has placed these models into automated systems to ensure access for employees, human resources professionals, and managers. Shared access to the data creates a shared frame of reference and a common language of competencies that have provided the basis for competency applications in public sector agencies. © 2002 Wiley Periodicals, Inc.


Progressive Stochastic Learning for Noisy Labels

Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from “easy” tasks to “complex” tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from “reliable” labels to “noisy” labels. To realize POSTAL mechanism, we design a cluster of “screening losses,” which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI1 simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.1UCI is the abbreviation of University of California Irvine.


JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling

Dexterous surgical activity is of interest to many researchers in human motion modeling. In this paper, we describe a dataset of surgical activities and release it for public use. The dataset was captured using the da Vinci Surgical System and consists of kinematic and video from eight surgeons with different levels of skill performing five repetitions of three elementary surgical tasks on a bench-top model. The tasks, which include suturing, knot-tying and needle-passing, are standard components of most surgical skills training curricula. In addition to kinematic and video data captured from the da Vinci Surgical System, we are also releasing manual annotations of surgical gestures (atomic activity segments), surgical skill using global rating scores, a standardized cross-validation experimental setup, and a C++/Matlab toolkits for analyzing surgical gestures using hidden Markov models and using linear dynamical systems. We refer to the dataset as the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) to indicate the collaboration between Johns Hopkins University (JHU) and Intuitive Surgical Inc. (ISI), Sunnyvale, CA, on collecting these data.


Robust model predictive control for an uncertain smart thermal grid

The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.


Toward Open Set Recognition

To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of “closed set” recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is “open set” recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel “1-vs-set machine,” which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.


Input Warping for Bayesian Optimization of Non-stationary Functions

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions. The ability to accurately model distributions over functions is critical to the effectiveness of Bayesian optimization. Although Gaussian processes provide a flexible prior over functions, there are various classes of functions that remain difficult to model. One of the most frequently occurring of these is the class of non-stationary functions. The optimization of the hyperparameters of machine learning algorithms is a problem domain in which parameters are often manually transformed a priori, for example by optimizing in “log-space,” to mitigate the effects of spatially-varying length scale. We develop a methodology for automatically learning a wide family of bijective transformations or warpings of the input space using the Beta cumulative distribution function. We further extend the warping framework to multi-task Bayesian optimization so that multiple tasks can be warped into a jointly stationary space. On a set of challenging benchmark optimization tasks, we observe that the inclusion of warping greatly improves on the state-of-the-art, producing better results faster and more reliably.


CVAP: Validation for Cluster Analyses

Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and procedures) and an analysis environment for clustering, evaluation of clustering results, estimation of the number of clusters, and performance comparison among different clustering algorithms. It can help users accomplish their clustering tasks faster and easier and help achieve good clustering quality when there is little prior knowledge about the cluster structure of a data set.


A New High-Temperature Superconducting Vernier Permanent-Magnet Machine for Wind Turbines

A vernier permanent magnet (VPM) machine using high-temperature superconducting (HTS) winding for dual excitation is proposed for wind power generation. The proposed VPM machine adopts the parallel hybrid excitation configuration, the stator consists of an identical tooth pair connected together by the stator yoke and the rotor contains two sets of consequent-pole permanent magnets joined together by the rotor yoke. The armature winding is located at the stator main slots while the field winding using HTS coils is wound on the stator yoke. With the benefit of the HTS field winding, the air-gap flux can be flexibly adjusted by controlling the magnitude of the dc current. Therefore, the machine performance is improved to cope with different operating conditions.


The Neglect of Power in Recent Framing Research By

This article provides a critique of recent developments in research examining media frames and their influence. We contend that a number of trends in framing research have neglected the relationship between media frames and broader issues of political and social power. This neglect is a product of a number of factors, including conceptual problems in the definition of frames, the inattention to frames sponsorship, the failure to examine framing contests within wider political and social contexts, and the reduction of framing to a form of media effects. We conclude that framing research needs to be linked to the political and social questions regarding power central to the media hegemony thesis, and illustrate this focus by exploring how framing research can contribute to an understanding of the interaction between social movements and the news media.


Research on the Matthews Correlation Coefficients Metrics of Personalized Recommendation Algorithm Evaluation

The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn’t fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What’s more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm’s performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm.


Implementation of a fuzzy logic speed controller for a permanent magnet dc motor using a low-cost Arduino platform

Implementation of a Fuzzy Logic speed controller for a Permanent Magnet DC (PMDC) motor using a low-cost Arduino interfaced with a standard xPC Target MATLAB environment is demonstrated. The Arduino solution is acceptable at kHz sampling frequencies. The study of speed control of PMDC motor based on Arduino and L298N H-Bridge using an artificial intelligence control method, such as Fuzzy PID with only four rules, has been validated by experiments on the physical system.


An embedded, GSM based, multiparameter, realtime patient monitoring system and control — An implementation for ICU patients

Wireless, remote patient monitoring system and control using feedback and GSM technology is used to monitor the different parameters of an ICU patient remotely and also control over medicine dosage is provided. Measurement of vital parameters can be done remotely and under risk developing situation can be conveyed to the physician with alarm triggering systems in order to initiate the proper control actions. In the implemented system a reliable and efficient real time remote patient monitoring system that can play a vital role in providing better patient care is developed. This system enables expert doctors to monitor vital parameters viz body temperature, blood pressure and heart rate of patients in remote areas of hospital as well as he can monitor the patient when he is out of the premises. The system in addition also provides a feedback to control the dosage of medicine to the patient as guided by the doctor remotely, in response to the health condition message received by the doctor. Mobile phones transfer measured parameters via SMS to clinicians for further analysis or diagnosis. The timely manner of conveying the real time monitored parameter to the doctor and control action taken by him is given high priority which is very much needed and which is the uniqueness of the developed system. The system even facilitates the doctor to monitor the patient's previous history from the data in memory inbuilt in the monitoring device. Also data can be sent to several doctors incase a doctor fails to respond urgently.


Exploring the relationship between knowledge management practices and innovation performance

The process of innovation depends heavily on knowledge, and the management of knowledge and human capital should be an essential element of running any type of business. Recent research indicates that organisations are not consistent in their approach to knowledge management (KM), with KM approaches being driven predominantly within an information technology (IT) or humanist framework, with little if any overlap. This paper explores the relationship between KM approaches and innovation performance through a preliminary study focusing on the manufacturing industry. The most significant implication that has emerged from the study is that managers in manufacturing firms should place more emphasis on human resource management (HRM) practices when developing innovation strategies for product and process innovations. The study shows that KM contributes to innovation performance when a simultaneous approach of “soft HRM practices” and “hard IT practices” are implemented.


Schedulability Analysis Using Uppaal: Herschel-Planck Case Study

We propose a modeling framework for performing schedulability analysis by using Uppaal real-time model-checker [2]. The framework is inspired by a case study where schedulability analysis of a satellite system is performed. The framework assumes a single CPU hardware where a fixed priority preemptive scheduler is used in a combination with two resource sharing protocols and in addition voluntary task suspension is considered. The contributions include the modeling framework, its application on an industrial case study and a comparison of results with classical response time analysis.