Internet Privacy Concerns Confirm the Case for Intervention

yberspace is invading private space. Controversies about spam, cookies, and the clickstream are merely the tip of an iceberg. Behind them loom real-time person location technologies includ-It's small wonder that lack of public confidence is a serious impediment to the take-up rate of consumer e-commerce. The concerns are not merely about security of value, but about something much more significant: trust in the information society. Conventional thinking has been the Internet renders laws less relevant. On the contrary, this article argues that the current debates about privacy and the Internet are the harbingers of a substantial shift. Because the U.S. has held off general privacy protections for so long, it will undergo much more significant adjustments than European countries. Privacy is often thought of as a moral right or a legal right. But it's often more useful to perceive privacy as the interest that individuals have in sustaining a personal space, free from interference by other people and organizations. Personal space has multiple dimensions, in particular , privacy of the person (concerned with the integrity of the individual's body), privacy of personal behavior, privacy of personal communications , and privacy of personal data. Information privacy refers to the claims of individuals that data about themselves should generally not be available to other individuals and organizations, and that, where data is possessed by another party, the individual must be able to exercise a substantial degree of control over that data and its use. (Definitional issues are examined in [6].) Information privacy has been under increasing threat as a result of the rapid replacement of expensive physical surveillance by what I referred to in Communications over a decade ago as " dataveillance: " the systematic use of personal data systems in the investigation or monitoring of people's actions or communications [2]. Intensive data trails about each individual provide a basis for the exercise of power over I In nt te er rv ve en nt ti io on n Public confidence in matters of online privacy seemingly lessens as the Internet grows. Indeed, there is mounting evidence the necessary remedy may be a protective framework that includes (gulp) legislative provisions.


An Engineering Model for Color Difference as a Function of Size

This work describes a first step towards the creation of an engineering model for the perception of color difference as a function of size. Our approach is to non-uniformly rescale CIELAB using data from crowdsourced experiments, such as those run on Amazon Mechanical Turk. In such experiments, the inevitable variations in viewing conditions reflect the environment many applications must run in. Our goal is to create a useful model for design applications where it is important to make colors distinct, but for which a small set of highly distinct colors is inadequate.


Generating UML Diagrams from Natural Language Specifications

The process of generating UML Diagrams from natural language specification is a highly challenging task. This paper proposes a method and tool to facilitate the requirements analysis process and extract UML diagrams from textual requirements using natural language processing (NLP) and Domain Ontology techniques. Requirements engineers analyze requirements manually to understand the scope of the system. The time spent on the analysis and the low quality of human analysis justifies the need of a tool for better understanding of the system. “Requirement analysis to Provide Instant Diagrams (RAPID)” is a desktop tool to assist requirements analysts and Software Engineering students to analyze textual requirements, finding core concepts and its relationships, and extraction UML diagrams. The evaluation of RAPID system is in the process and will be conducted through two forms of evaluation, experimental and expert evaluation.


Chatbot for admissions

The communication of potential students with a university department is performed manually and it is a very time consuming procedure. The opportunity to communicate with on a one-to-one basis is highly valued. However with many hundreds of applications each year, one-to-one conversations are not feasible in most cases. The communication will require a member of academic staff to expend several hours to find suitable answers and contact each student. It would be useful to reduce his costs and time. The project aims to reduce the burden on the head of admissions, and potentially other users, by developing a convincing chatbot. A suitable algorithm must be devised to search through the set of data and find a potential answer. The program then replies to the user and provides a relevant web link if the user is not satisfied by the answer. Furthermore a web interface is provided for both users and an administrator. The achievements of the project can be summarised as follows. To prepare the background of the project a literature review was undertaken, together with an investigation of existing tools, and consultation with the head of admissions. The requirements of the system were established and a range of algorithms and tools were investigated, including keyword and template matching. An algorithm that combines keyword matching with string similarity has been developed. A usable system using the proposed algorithm has been implemented. The system was evaluated by keeping logs of questions and answers and by feedback received by potential students that used it. 3 Acknowledgements I would like to thank Dr Peter Hancox for his immeasurable help and support throughout this project. I also need to express my thanks to the computer support team for their excellent help and instructions. Finally, I feel the need to acknowledge the constant support offered by my parents. Introduction This chapter covers an introduction to the project including the context, a description of aims and objectives, a description of what has been achieved, contributions and the structure of the report. Although the admissions process works properly as it is, it is very difficult and time consuming to contact a member of staff of the university. However, the problem would be partially solved if the applicant could talk to a convincing chatbot, able to respond to their concerns with information about admissions, booking accommodation, paying fees in instalments and what pre-sessional courses are on offer. The …


Levofloxacin implants with predefined microstructure fabricated by three-dimensional printing technique.

A novel three-dimensional (3D) printing technique was utilized in the preparation of drug implants that can be designed to have complex drug release profiles. The method we describe is based on a lactic acid polymer matrix with a predefined microstructure that is amenable to rapid prototyping and fabrication. We describe how the process parameters, especially selection of the binder, were optimized. Implants containing levofloxacin (LVFX) with predefined microstructures using an optimized binder solution of ethanol and acetone (20:80, v/v) were prepared by a 3D printing process that achieved a bi-modal profile displaying both pulsatile and steady state LVFX release from a single implant. The pulse release appeared from day 5 to 25, followed by a steady state phase of 25 days. The next pulse release phase then began at the 50th day and ended at the 80th day. To evaluate the drug implants structurally and analytically, the microscopic morphologies and the in vitro release profiles of the implants fabricated by both the 3D printing technique and the conventional lost mold technique were assessed using environmental scanning electron microscopy (ESEM) and UV absorbance spectrophotometry. The results demonstrate that the 3D printing technology can be used to fabricate drug implants with sophisticated micro- and macro-architecture in a single device that may be rapidly prototyped and fabricated. We conclude that drug implants with predefined microstructure fabricated by 3D printing techniques can have clear advantages compared to implants fabricated by conventional compressing methods.


A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue

We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NPand non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.


Smart Home Automated Control System Using Android Application and Microcontroller

Smart Home System (SHS) is a dwelling incorporating a communications network that connects the electrical appliances and services allowing them to be remotely controlled, monitored or accessed. SHS includes different approaches to achieve multiple objectives range from enhancing comfort in daily life to enabling a more independent life for elderly and handicapped people. In this paper, the main four fields for SHS which are, home automation and remote monitoring, environmental monitoring, including humidity, temperature, fault tracking and management and finally the health monitoring have been considered. The system design is based on the Microcontroller MIKRO C software; multiple passive and active sensors and also a wireless internet services which is used in different monitoring and control processes .This paper presents the hardware implementation of a multiplatform control system for house automation and combines both hardware and software technologies. The system results shows that it can be classified as a comfortable, secure, private, economic and safe system in addition to its great flexibility and reliability.


Regression Shrinkage and Selection via the Elastic Net , with Applications to Microarrays

We propose the elastic net, a new regression shrinkage and selection method. Real data and a simulation study show that the elastic net often outperforms the lasso, while it enjoys a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strong correlated predictors are kept in the model. The elastic net is particularly useful in the analysis of microarray data in which the number of genes (predictors) is much bigger than the number of samples (observations). We show how the elastic net can be used to construct a classification rule and do automatic gene selection at the same time in microarray data, where the lasso is not very satisfied. We also propose an efficient algorithm for solving the elastic net based on the recently invented LARS algorithm. keywords: Gene selection; Grouping effect; Lasso; LARS algorithm; Microarray classification.


Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's “tactile sensor.” This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.


Obsessive-compulsive disorder and gut microbiota dysregulation.

Obsessive-compulsive disorder (OCD) is a debilitating disorder for which the cause is not known and treatment options are modestly beneficial. A hypothesis is presented wherein the root cause of OCD is proposed to be a dysfunction of the gut microbiome constituency resulting in a susceptibility to obsessional thinking. Both stress and antibiotics are proposed as mechanisms by which gut microbiota are altered preceding the onset of OCD symptomology. In this light, pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) leading to episodic OCD is explained not by group A beta-hemolytic streptococcal infections, but rather by prophylactic antibiotics that are administered as treatment. Further, stressful life events known to trigger OCD, such as pregnancy, are recast to show the possibility of altering gut microbiota prior to onset of OCD symptoms. Suggested treatment for OCD would be the directed, specie-specific (re)introduction of beneficial bacteria modifying the gut microbiome, thereby ameliorating OCD symptoms. Special considerations should be contemplated when considering efficacy of treatment, particularly the unhealthy coping strategies often observed in patients with chronic OCD that may need addressing in conjunction with microbiome remediation.


Event-Based Mobile Social Networks: Services, Technologies, and Applications

Event-based mobile social networks (MSNs) are a special type of MSN that has an immanently temporal common feature, which allows any smart phone user to create events to share group messaging, locations, photos, and insights among participants. The emergence of Internet of Things and event-based social applications integrated with context-awareness ability can be helpful in planning and organizing social events like meetings, conferences, and tradeshows. This paper first provides review of the event-based social networks and the basic principles and architecture of event-based MSNs. Next, event-based MSNs with smartphone contained technology elements, such as context-aware mobility and multimedia sharing, are presented. By combining the feature of context-aware mobility with multimedia sharing in event-based MSNs, event organizers, and planners with the service providers optimize their capability to recognize value for the multimedia services they deliver. The unique features of the current event-based MSNs give rise to the major technology trends to watch for designing applications. These mobile applications and their main features are described. At the end, discussions on the evaluation of the event-based mobile applications based on their main features are presented. Some open research issues and challenges in this important area of research are also outlined.


Fully Integrated Startup at 70 mV of Boost Converters for Thermoelectric Energy Harvesting

This paper presents an inductive DC-DC boost converter for energy harvesting using a thermoelectric generator with a minimum startup voltage of 70 mV and a regulated output voltage of 1.25 V. With a typical generator resistance of 40 Ω, an output power of 17 μW can be provided, which translates to an end-to-end efficiency of 58%. The converter employs Schmitt-trigger logic startup control circuitry and an ultra-low voltage charge pump using modified Schmitt-trigger driving circuits optimized for driving capacitive loads. Together with a novel ultra-low leakage power switch and the required control scheme, to the best of the authors' knowledge, this enables the lowest minimum voltage with fully integrated startup.


Adaptation and the SetPoint Model of Subjective Well-Being Does Happiness Change After Major Life Events ?

Hedonic adaptation refers to the process by which individuals return to baseline levels of happiness following a change in life circumstances. Dominant models of subjective well-being (SWB) suggest that people can adapt to almost any life event and that happiness levels fluctuate around a biologically determined set point that rarely changes. Recent evidence from large-scale panel studies challenges aspects of this conclusion. Although inborn factors certainly matter and some adaptation does occur, events such as divorce, death of a spouse, unemployment, and disability are associated with lasting changes in SWB. These recent studies also show that there are considerable individual differences in the extent to which people adapt. Thus, happiness levels do change, and adaptation is not inevitable. KEYWORDS—happiness; subjective well-being; adaptation; set-point theory People’s greatest hopes and fears often center on the possible occurrence of rare but important life events. People may dread the possibility of losing a loved one or becoming disabled, and they may go to great lengths to find true love or to increase their chances of winning the lottery. In many cases, people strive to attain or avoid these outcomes because of the outcomes’ presumed effect on happiness. But do these major life events really affect long-term levels of subjective well-being (SWB)? Dominant models of SWB suggest that after experiencing major life events, people inevitably adapt. More specifically, set-point theorists posit that inborn personality factors cause an inevitable return to genetically determined happiness set points. However, recent evidence from large-scale longitudinal studies challenges some of the stronger conclusions from these models. ADAPTATION RESEARCH AND THEORY Although the thought that levels of happiness cannot change may distress some people, researchers believe that adaptation processes serve important functions (Frederick & Loewenstein, 1999). For one thing, these processes protect people from potentially dangerous psychological and physiological consequences of prolonged emotional states. In addition, because adaptation processes allow unchanging stimuli to fade into the attentional background, these processes ensure that change in the environment receives extra attention. Attention to environmental change is advantageous because threats that have existed for prolonged periods of time are likely to be less dangerous than novel threats. Similarly, because rewards that have persisted are less likely to disappear quickly than are novel rewards, it will often be advantageous to attend and react more strongly to these novel rewards. Finally, by reducing emotional reactions over time, adaptation processes allow individuals to disengage from goals that have little chance of success. Thus, adaptation can be beneficial, and some amount of adaptation to life circumstances surely occurs. Yet many questions about the strength and ubiquity of adaptation effects remain, partly because of the types of evidence that have been used to support adaptation theories. In many cases, adaptation is not directly observed. Instead, it must be inferred from indirect evidence. For instance, psychologists often cite the low correlation between happiness and life circumstances as evidence for adaptation effects. Factors such as income, age, health, marital status, and number of friends account for only a small percentage of the variance in SWB (Diener, Suh, Lucas, & Smith, 1999). One explanation that has been offered for this counterintuitive finding is that these factors initially have an impact but that people adapt over time. However, the weak associations between life circumstances and SWB themselves provide only suggestive evidence for this explanation. Additional indirect support for the set-point model comes from research that takes a personality perspective on SWB. Address correspondence to Richard E. Lucas, Department of Psychology, Michigan State University, East Lansing, MI 48823; e-mail: lucasri@msu.edu. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Volume 16—Number 2 75 Copyright r 2007 Association for Psychological Science Three pieces of evidence are relevant (Lucas, in press-b). First, SWB exhibits moderate stability even over very long periods of time and even in the face of changing life circumstances. Recent reviews suggest that approximately 30 to 40% of the variance in life-satisfaction measures is stable over periods as long as 20 years. Second, a number of studies have shown that well-being variables are about 40 to 50% heritable. These heritability estimates appear to be even higher (about 80%) for long-term levels of happiness (Lykken & Tellegen, 1996). Finally, personality variables like extroversion and neuroticism are relatively strong predictors of happiness, at least when compared to the predictive power of external factors. The explanation for this set of findings is that events can influence short-term levels of happiness, but personality-based adaptation processes inevitably move people back to their genetically determined set point after a relatively short period of time. More direct evidence for hedonic adaptation comes from studies that examine the well-being of individuals who have experienced important life events. However, even these studies can be somewhat equivocal. For instance, one of the most famous studies is that of Brickman, Coates, and Janoff-Bulman (1978) comparing lottery winners and patients with spinal-cord injuries to people in a control group. Brickman et al. showed that lottery winners were not significantly happier than the control-group participants and that individuals with spinal-cord injuries ‘‘did not appear nearly as unhappy as might be expected’’ (p. 921). This study appears to show adaptation to even the most extreme events imaginable. What is often not mentioned, however, is that although the participants with spinal-cord injuries were above neutral on the happiness scale (which is what led Brickman et al. to conclude that they were happier than might be expected), they were significantly less happy than the people in the control group, and the difference between the groups was actually quite large. Individuals with spinal-cord injuries were more than three quarters of a standard deviation below the mean of the control group. This means that the average participant from the control group was happier than approximately 78% of participants with spinal-cord injuries. This result has now been replicated quite often—most existing studies show relatively large differences between individuals with spinal-cord injuries and healthy participants in control groups (Dijkers, 1997). In addition to problems that result from the interpretation of effect sizes, methodological limitations restrict the conclusions that can be drawn from many existing studies of adaptation. Most studies are not longitudinal, and even fewer are prospective (though there are some notable exceptions; see e.g., Bonanno, 2004; Caspi et al., 2003). Because participants’ pre-event levels of SWB are not known, it is always possible that individuals who experienced an event were more or less happy than average before the event occurred. Certain people may be predisposed to experience life events, and these predisposing factors may be responsible for their happiness levels being lower than average. For instance, in a review of the literature examining the well-being of children who had lost limbs from various causes, Tyc (1992) suggested that those who lost limbs due to accidents tended to have higher levels of premorbid psychological disorders than did those who lost limbs due to disease. Thus, simply comparing the well-being of children who lost limbs to those who did not might overestimate the effect of the injury. Psychologists have demonstrated that level of happiness predicts the occurrence of a variety of events and outcomes (Lyubomirsky, King, & Diener, 2005), and therefore, studies that compare individuals who have experienced a particular event with those who have not but that do not take into account previous happiness level must be interpreted cautiously. A second methodological concern relates to what are known as demand characteristics. When researchers recruit participants specifically because they have experienced a given life event, participants may overor underreport SWB. These reports may occur because people believe the life event should have an impact, because they want to appear well-adjusted, or simply because the context of the study makes the event more salient. For instance, Smith, Schwarz, Roberts, and Ubel (2006) showed that patients with Parkinson’s disease reported lower life satisfaction when the study instructions indicated that Parkinson’s disease was a focus than when the instructions indicated that the study focused on the general population. USING LARGE-SCALE PANEL STUDIES TO ASSESS ADAPTATION TO LIFE EVENTS Recently, my colleagues and I have turned to archival data analysis using large, nationally representative panel studies to address questions about adaptation to life events. These studies have a number of advantages over alternative designs. First, they are prospective, which means that pre-event levels of SWB are known. Second, they are longitudinal, which means that change over time can be accurately modeled. Third, very large samples are often involved, which means that even rare events are sampled. Finally, because designers of these studies often recruit nationally representative samples, and because the questionnaires often focus on a variety of issues, demand characteristics are unlikely to have much of an effect. We have used two such panel studies—the German Socioeconomic Panel Study (GSOEP) and the British Household Panel Study (BHPS)—to examine the amount of adaptation that occurs following major life events. The GSOEP includes almost 40,000 individuals living in Germ


Comparison of perioperative outcomes between robotic and laparoscopic partial nephrectomy: a systematic review and meta-analysis.

CONTEXT
Robotic partial nephrectomy (RPN) is rapidly increasing; however, the benefit of RPN over laparoscopic partial nephrectomy (LPN) is controversial.

OBJECTIVE
To compare perioperative outcomes of RPN and LPN.

EVIDENCE ACQUISITION
We searched Ovid-Medline, Ovid-Embase, the Cochrane Library, KoreaMed, KMbase, KISS, RISS, and KisTi from their inception through August 2013. Two independent reviewers extracted data using a standardized form. Quality of the selected studies was assessed using the methodological index for nonrandomized studies.

EVIDENCE SYNTHESIS
A total of 23 studies and 2240 patients were included. All studies were cohort studies with no randomization, and the methodological quality varied. There was no significant difference between the two groups regarding complications of Clavien-Dindo classification grades 1-2 (p=0.62), Clavien-Dindo classification grades 3-5 (p=0.78), change of serum creatinine (p=0.65), operative time (p=0.35), estimated blood loss (p=0.76), and positive margins (p=0.75). The RPN group had a significantly lower rate of conversion to open surgery (p=0.02) and conversion to radical surgery (p=0.0006), shorter warm ischemia time (WIT; p=0.005), smaller change of estimated glomerular filtration rate (eGFR; p=0.03), and shorter length of stay (LOS; p=0.004).

CONCLUSIONS
This meta-analysis shows that RPN is associated with more favorable results than LPN in conversion rate to open or radical surgery, WIT, change of eGFR, and shorter LOS. To establish the safety and effectiveness outcomes of robotic surgery, well-designed randomized clinical studies with long-term follow-up are needed.

PATIENT SUMMARY
Robotic partial nephrectomy (PN) is more favorable than laparoscopic PN in terms of lower conversion rate to radical nephrectomy, a favorable renal function indexed estimated glomerular filtration rate, shorter length of hospital stay, and shorter warm ischemia time.


Bonding-wire triangular spiral inductor for on-chip switching power converters

This work presents the first design and modelling of bonding-wire-based triangular spiral inductors (Fig. 1), targeting their application to on-chip switching power converters. It is demonstrated that the equilateral triangular shape compared to other polygonal shapes best balances the inductive density as well as the total Equivalent Series Resistance (ESR). Afterwards, a design procedure is presented in order to optimize the inductor design, in terms of ESR and occupied area reduction. Finally, finite-elements simulation results of an optimized design (27nH, 1 Ω) are presented to validate the proposed expressions.


Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

Article history: Received 26 August 2007 Received in revised form 7 May 2008 Accepted 13 May 2008


Formal Analysis of Enhanced Authorization in the TPM 2.0

The Trusted Platform Module (TPM) is a system component that provides a hardware-based approach to establish trust in a platform by providing protected storage, robust platform integrity measurement, secure platform attestation and other secure functionalities. The access to TPM commands and TPM-resident key objects are protected via an authorization mechanism. Enhanced Authorization (EA) is a new mechanism introduced by the TPM 2.0 to provide a rich authorization model for specifying flexible access control policies for TPM-resident objects.
 In our paper, we conduct a formal verification of the EA mechanism. Firstly, we propose a model of the TPM 2.0 EA mechanism in a variant of the applied pi calculus. Secondly, we identify and formalize the security properties of the EA mechanism (Prop.1 and 2) in its design. We also give out a misuse problem that is easily to be neglected (Lemma 7). Thirdly, using the SAPIC tool and the tamarin prover, we have verified both the two security properties. Meanwhile, we have found 3 misuse cases and one of them leads to an attack on the application in [12].


RUNNING HEAD : THE SOCIAL ORIENTATION HYPOTHESIS-1The Origin of Cultural Differences in Cognition : The Social Orientation Hypothesis

A large body of research documents cognitive differences between Westerners and East Asians. Westerners tend to be more analytic and East Asians tend to be more holistic. These findings have often been explained as being due to corresponding differences in social orientation. Westerners are more independent and Easterners are more interdependent. However, comparisons of the cognitive tendencies of Westerners and East Asians do not allow us to rule out alternative explanations for the cognitive differences, such as linguistic and genetic differences, as well as cultural differences other than social orientation. In this review we summarize recent developments that provide stronger support for the social-orientation hypothesis. Keywordsculture; cross-cultural differences; within-culture differences; reasoning; independence/interdependence; holistic/analytic cognition RUNNING HEAD: THE SOCIAL ORIENTATION HYPOTHESIS 3 Cultural psychologists have consistently found different patterns of thinking and perception in different societies, with some cultures demonstrating a more analytic pattern and others a more holistic pattern (see Table 1). Analytic cognition is characterized by taxonomic and rule-based categorization of objects, a narrow focus in visual attention, dispositional bias in causal attribution, and the use of formal logic in reasoning. In contrast, holistic cognition is characterized by thematic and familyresemblance-based categorization of objects, a focus on contextual information and relationships in visual attention, an emphasis on situational causes in attribution, and dialecticism (Nisbett, Peng, Choi, & Norenzayan, 2001). What unites the elements of the analytic style is a tendency to focus on a single dimension or aspect—whether in categorizing objects or evaluating arguments—and a tendency to disentangle phenomena from the contexts in which they are embedded—for example, focusing on the individual as a causal agent or attending to focal objects in visual scenes. What unites the elements of the holistic style is a broad attention to context and relationships in visual attention, categorizing objects, and explaining social behavior. Table 1 about here Cultures also differ in their social orientations (independence vs. interdependence) (see Table 2). Cultures that endorse and afford independent social orientation tend to emphasize self-direction, autonomy, and self-expression. Cultures that endorse and afford interdependent social orientation tend to emphasize harmony, relatedness, and connection. Independently oriented cultures tend to view the self as bounded and separate from social others, whereas interdependently oriented cultures tend to view the self as interconnected and as encompassing important relationships (e.g. Markus & Kitayama, 1991; Triandis, 1989). In independently oriented cultural contexts, happiness is most often experienced as a socially disengaging emotion (i.e. pride), whereas in interdependently oriented cultural contexts, happiness is most often experienced as a RUNNING HEAD: THE SOCIAL ORIENTATION HYPOTHESIS 4 socially engaging emotion (i.e. sense of closeness to others; Kitayama, Mesquita, & Karasawa, 2006). Finally, in cultures that have an independent social orientation, people are more motivated to symbolically enhance the self at the expense of others; this tendency is not as common in interdependently oriented cultures (Kitayama, Ishii, Imada, Takemura, & Ramaswamy, 2006; Kitayama, Mesquita, & Karasawa, 2006). Table 2 about here The proposition that cultures differing in their social orientation (independence vs. interdependence) also differ in their cognitive habits (analytic vs. holistic cognition) is by no means new (e.g. Markus & Kitayama, 1991; Witkin & Berry, 1975). Indeed one can trace the origin of this claim back at least to Tönnies (/2002). And certainly a large body of literature has demonstrated that cultures which differ in social orientation also show corresponding differences in cognitive style; Western societies tend to be more independent and more analytic, while East Asian societies tend to be more interdependent and holistic (Nisbett et al., 2001). On the basis of such evidence, it has been proposed that differences in social orientation are the driving force behind cultural differences in cognition (Markus & Kitayama, 1991; Nisbett et al., 2001). While the link between social orientation and cognitive style has been widely accepted, the evidence presented until recently has not provided strong support for this connection. East Asia and the West are huge geographic and cultural areas differing from one another in many ways. There are fairly large genetic differences between the two populations. The linguistic differences are large. Western languages are almost all IndoEuropean in origin and differ in many systematic ways from the major languages of East Asia. And there are many large cultural differences between the two regions other than in social orientation along lines of independence and interdependence. East Asia was heavily influenced by Confucian values and ways of thought and European cultures were heavily influenced by ancient Greek, specifically Aristotelian, values and ways of RUNNING HEAD: THE SOCIAL ORIENTATION HYPOTHESIS 5 thought (Lloyd, 1996). Just within this broad set of cultural differences it would be possible to find many hypotheses that might account for the kind of cognitive differences that have been observed between East and West. Examples of other large societal differences between East and West have to do with the length of time that the respective societies have been industrialized and the degree to which political institutions in these societies have a tradition of being democratic. Both of these latter dimensions are frequently invoked to account for a host of differences between East and West. In the present review, we focus on recent studies that narrow the plausible range of candidates for explaining the cognitive differences. These studies look at much tighter cultural comparisons than those found in previous research. These studies compare Eastern and Western Europe, Europe with the United States, northern and southern Italy, Hokkaido and Mainland Japan, adjacent villages in Turkey, and middle-class and working-class Americans. All of these comparisons involve contrasting more interdependent cultures with more independent cultures. We also review research that manipulates independence vs. interdependence and finds differences in analytic vs. holistic cognition The recent studies make it much less likely that the cognitive differences observed between East and West are due to large genetic or linguistic differences and make it more plausible that the cognitive differences are indeed due to differences in social orientation having to do with independence vs. interdependence rather than to societal differences such as Aristotelian vs. Confucian intellectual traditionsor degree of industrialization. RUNNING HEAD: THE SOCIAL ORIENTATION HYPOTHESIS 6 CROSS-CULTURAL COMPARISONS Several recent studies have shown that the covariation between social orientation and cognitive style is not confined to North America and East Asia. Even within societies that are part of the European cultural tradition, one observes that cultures differing in social orientation also differ in terms of cognitive style. For example, East Europeans and Americans differ along these dimensions. Russians are more interdependent than Americans (Grossmann, 2009; Matsumoto, Takeuchi, Andayani, Kouznetsova, & Krupp, 1998) and are more holistic in terms of categorization, attribution, visual attention, and reasoning about change (Grossmann, 2009). Similarly, Croats are more interdependent than Americans (Šverko, 1995) and show more holistic patterns of cognition in terms of categorization and visual attention (Varnum, Grossmann, Katunar, Nisbett, & Kitayama, 2008). Recent evidence suggests that similar differences exist within Europe. Russians, who are more interdependent than Germans (Naumov, 1996), also show more contextual patterns of visual attention (Medzheritskaya, 2008). WITHIN-CULTURE DIFFERENCES The fact that social orientation and cognitive style covary in comparisons across and within broad cultural regions does not fully address alternative explanations for this pattern. Cross-cultural differences in cognition might conceivably be accounted for by differences in linguistics, genetics, and degree and recency of industrialization and democratization. However, studies comparing groups within the same culture tend to argue against such interpretations. In a recent study comparing Hokkaido Japanese with those from mainland Japan, Kitayama and colleagues (Kitayama, Ishii, et al., 2006) found that those from Hokkaido RUNNING HEAD: THE SOCIAL ORIENTATION HYPOTHESIS 7 (settled by pioneers from the southern Japanese islands) were more independent than those from the main islands and also showed more dispositional bias in attribution. Similarly, Northern Italians, who are more independent than Southern Italians (Martella & Maass, 2000), also show more analytic cognitive habits, categorizing objects in a more taxonomic fashion (Knight & Nisbett, 2007). Even more fine-grained comparisons have found that, within a culture, groups differing in social orientation also differ in cognitive style. For example, Uskul and colleagues compared neighboring villages in the Black Sea region of Turkey that differed in terms of their primary economic activity (Uskul, Kitayama, & Nisbett, 2008). Previous research has found that more sedentary communities (such as farming communities and cooperative fishing communities) tend to be characterized by a more interdependent social orientation and holistic cognition (specifically field dependence or the tendency to have difficulty separating objects from their contexts; Berry, 1966; Witkin & Berry, 1975). Less sedentary communiti


Application of Data Mining Techniques in IoT: A Short Review

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Interfacing an every object together through internet looks very difficult, but within a frame of time Internet of Things will drastically change our life. The enormous data captured by the Internet of Things (IoT) are considered of high business as well as social values and extracting hidden information from raw data, various data mining algorithm can be applied to IoT data. In this paper, We survey systematic review of various data mining models as well as its application in Internet of Thing (IoT) field along with its merits and demerits. At last, we discussed challenges in IoT.


Large scale metric learning from equivalence constraints

In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-of-the-art.


The AR face database


Universal approximation using incremental constructive feedforward networks with random hidden nodes

According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g : R --> R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g : R --> R and integral of R g(x)dx not equal to 0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.


Robust Discriminative Response Map Fitting with Constrained Local Models

We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms state-of-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.


Permission Re-Delegation: Attacks and Defenses

Modern browsers and smartphone operating systems treat applications as mutually untrusting, potentially malicious principals. Applications are (1) isolated except for explicit IPC or inter-application communication channels and (2) unprivileged by default, requiring user permission for additional privileges. Although inter-application communication supports useful collaboration, it also introduces the risk of permission redelegation. Permission re-delegation occurs when an application with permissions performs a privileged task for an application without permissions. This undermines the requirement that the user approve each application’s access to privileged devices and data. We discuss permission re-delegation and demonstrate its risk by launching real-world attacks on Android system applications; several of the vulnerabilities have been confirmed as bugs. We discuss possible ways to address permission redelegation and present IPC Inspection, a new OS mechanism for defending against permission re-delegation. IPC Inspection prevents opportunities for permission redelegation by reducing an application’s permissions after it receives communication from a less privileged application. We have implemented IPC Inspection for a browser and Android, and we show that it prevents the attacks we found in the Android system applications.


Spatio-temporal proximity and social distance: a confirmation framework for social reporting

Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.


Modeling and Verification of a Six-Phase Interior Permanent Magnet Synchronous Motor

In this paper, a new mathematical modeling for a six-phase interior permanent magnet synchronous motor (IPMSM) is presented. The proposed model utilizes two synchronous reference frames. First, the flux model in the <inline-formula> <tex-math notation="LaTeX">$abcxyz$</tex-math></inline-formula> frame is mapped into the stationary <inline-formula> <tex-math notation="LaTeX">$dq$</tex-math></inline-formula> frames and then to two synchronous rotating frames. Then, differentiating the flux models, voltage equations are derived in rotating frames. Through this analysis, the interaction between the <inline-formula><tex-math notation="LaTeX">$abc$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$xyz$</tex-math></inline-formula> subsystems is properly described by a coupling matrix. The torque equation is also derived using the two reference current variables. Flux model was verified through FEM analysis. Experiments were done using a 100 kW six-phase IPMSM in a dynamo system. The validity of the torque equation was checked with some experimental results under a shorted condition on an <inline-formula><tex-math notation="LaTeX">$xyz$</tex-math></inline-formula> subsystem.


Autonomous exploration of motor skills by skill babbling

Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives.


NestedNet: Learning Nested Sparse Structures in Deep Neural Networks

Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements, i.e., anytime property. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.


Marketing actions can modulate neural representations of experienced pleasantness.

Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.


Automatic Transliteration of Proper Nouns from Arabic to English

After providing a brief introduction to the transliteration problem, and highlighting some issues specific to Arabic to English translation, a three phase algorithm is introduced as a computational solution to the problem. The algorithm is based on a Hidden Markov Model approach, but also leverages information available in on-line databases. The algorithm is then evaluated, and shown to achieve accuracy approaching 80%.


A glasses-type wearable device for monitoring the patterns of food intake and facial activity

Here we present a new method for automatic and objective monitoring of ingestive behaviors in comparison with other facial activities through load cells embedded in a pair of glasses, named GlasSense. Typically, activated by subtle contraction and relaxation of a temporalis muscle, there is a cyclic movement of the temporomandibular joint during mastication. However, such muscular signals are, in general, too weak to sense without amplification or an electromyographic analysis. To detect these oscillatory facial signals without any use of obtrusive device, we incorporated a load cell into each hinge which was used as a lever mechanism on both sides of the glasses. Thus, the signal measured at the load cells can detect the force amplified mechanically by the hinge. We demonstrated a proof-of-concept validation of the amplification by differentiating the force signals between the hinge and the temple. A pattern recognition was applied to extract statistical features and classify featured behavioral patterns, such as natural head movement, chewing, talking, and wink. The overall results showed that the average F1 score of the classification was about 94.0% and the accuracy above 89%. We believe this approach will be helpful for designing a non-intrusive and un-obtrusive eyewear-based ingestive behavior monitoring system.


A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids

Mining grid data is an interesting research field which aims at analyzing grid systems with data mining techniques in order to efficiently discover new meaningful knowledge to enhance grid management. In this paper, we focus particularly on how extracted knowledge enables enhancing data replication and replica selection strategies which are important data management techniques commonly used in data grids. Indeed, relevant knowledge such as file access patterns, file correlations, user or job access behavior, prediction of future behavior or network performance, and so on, can be efficiently discovered. These findings are then used to enhance both data replication and replica selection strategies. Various works in this respect are then discussed along with their merits and demerits. In addition, we propose a new guideline to data mining application in the context of data replication and replica selection strategies. & 2015 Elsevier Ltd. All rights reserved.


Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. Email addresses: fatemeh.lashkari@unb.ca (Fatemeh Lashkari), bagheri@ryerson.ca (Ebrahim Bagheri), ghorbani@unb.ca (Ali A. Ghorbani) Preprint submitted to Information Processing and Management September 11, 2018


Visualizing RFM Segmentation

Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and improve profits. RFM analysis is commonly performed using the Arthur Hughes method, which bins each of the three RFM attributes independently into five equal frequency bins. The resulting 125 cells are depicted in a tabular format or as bar graphs and analyzed by marketers, who determine the best cells (customer segments) to target. We propose an interactive visualization of RFM that helps marketers visualize and quickly identify important customer segments. Additionally, we show an integrated filtering approach that allows marketers to interactively explore the RFM segments in relation to other customer attributes, such as behavioral or demographic, to identify interesting subsegments in the customer base. We depict these RFM visualizations on two large real-world data sets and discuss how customers have used these visualizations in practice to glean interesting insights from their data. Given, the widespread use of RFM as a critical, and many times the only, segmentation tool, we believe that the proposed intuitive and interactive visualization will provide significant business value.


Prognostics and Health Management of Industrial Equipment

Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL).


Constraint-based sequential pattern mining: the pattern-growth methods

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.


Bad to the bone: facial structure predicts unethical behaviour.

Researchers spanning many scientific domains, including primatology, evolutionary biology and psychology, have sought to establish an evolutionary basis for morality. While researchers have identified social and cognitive adaptations that support ethical behaviour, a consensus has emerged that genetically determined physical traits are not reliable signals of unethical intentions or actions. Challenging this view, we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour if they are also associated with positive signals in intersex and intrasex selection. Specifically, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men. Across two studies, we demonstrate that men with wider faces (relative to facial height) are more likely to explicitly deceive their counterparts in a negotiation, and are more willing to cheat in order to increase their financial gain. Importantly, we provide evidence that the link between facial metrics and unethical behaviour is mediated by a psychological sense of power. Our results demonstrate that static physical attributes can indeed serve as reliable cues of immoral action, and provide additional support for the view that evolutionary forces shape ethical judgement and behaviour.


Impact of social media usage on students academic performance in Saudi Arabia

Social media is a popular method for communication amongst university students in Saudi Arabia. However excessive social media use can raise questions about whether academic performance is affected. This research explores this question by conducting a survey on university students in Saudi Arabia in regards to social media usage and their academic performance. The survey also explored which social network is the most popular amongst Saudi students, what students thought about their social media usage and factors besides social media usage which negatively affect academic performance. The survey received 108 responses and descriptive statistics including normality tests i.e. scatter plots were used to examine the relationship between the average number of hours students spent of social media a week and GPA scores of the students. The results demonstrated that there was no linear relationship between social media usage in a week and GPA score. Students highlighted that besides social media use, time management is a factor which affects students ‘studies negatively. The findings of the paper can be used to propose the effective plans for improving the academic performance of the students in such a way that a balance in the leisure, information exchange and academic performance can be maintained. 2014 Elsevier Ltd. All rights reserved.


Patterns of Play: Play-Personas in User-Centred Game Development

In recent years certain trends from User-Centered design have been seeping into the practice of designing computer games. The balance of power between game designers and players is being renegotiated in order to find a more active role for players and provide them with control in shaping the experiences that games are meant to evoke. A growing player agency can turn both into an increased sense of player immersion and potentially improve the chances of critical acclaim. This paper presents a possible solution to the challenge of involving the user in the design of interactive entertainment by adopting and adapting the "persona" framework introduced by Alan Cooper in the field of Human Computer Interaction. The original method is improved by complementing the traditional ethnographic descriptions of personas with parametric, quantitative, data-oriented models of patterns of user behaviour for computer games. Author


Common Consensus : a web-based game for collecting commonsense goals

In our research on Commonsense reasoning, we have found that an especially important kind of knowledge is knowledge about human goals. Especially when applying Commonsense reasoning to interface agents, we need to recognize goals from user actions (plan recognition), and generate sequences of actions that implement goals (planning). We also often need to answer more general questions about the situations in which goals occur, such as when and where a particular goal might be likely, or how long it is likely to take to achieve. In past work on Commonsense knowledge acquisition, users have been directly asked for such information. Recently, however, another approach has emerged—to entice users into playing games where supplying the knowledge is the means to scoring well in the game, thus motivating the players. This approach has been pioneered by Luis von Ahn and his colleagues, who refer to it as Human Computation. Common Consensus is a fun, self-sustaining web-based game, that both collects and validates Commonsense knowledge about everyday goals. It is based on the structure of the TV game show Family Feud1. A small user study showed that users find the game fun, knowledge quality is very good, and the rate of knowledge collection is rapid. ACM Classification: H.3.3 [INFORMATION STORAGE AND RETRIEVAL]: Information Search and Retrieval; I.2.6 [ARTIFICIAL INTELLIGENCE]: Learning


Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions. We tackle the problem of matrix completion when pairwise relationships among variables are known, via a graph. We formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods. On the theoretical front, we show that such methods generalize weighted nuclear norm formulations, and derive statistical consistency guarantees. We validate our results on both real and synthetic datasets.


Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.


Social collaborative filtering for cold-start recommendations

We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users.


Predicting purchase behaviors from social media

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.


Efficient Character Skew Rectification in Scene Text Images

We present an efficient method for character skew rectification in scene text images. The method is based on a novel skew estimators, which exploit intuitive glyph properties and which can be efficiently computed in a linear time. The estimators are evaluated on a synthetically generated data (including Latin, Cyrillic, Greek, Runic scripts) and real scene text images, where the skew rectification by the proposed method improves the accuracy of a state-of-the-art scene text recognition pipeline.


Why Do Those With Long-Term Substance Use Disorders Stop Abusing Substances? A Qualitative Study

Although a significant proportion of adults recover from substance use disorders (SUDs), little is known about how they reach this turning point or why they stop using. The purpose of the study was to explore the factors that influence reasoning and decision making about quitting substance use after a long-term SUD. Semistructured interviews were conducted with 18 participants, each of whom had been diagnosed with a SUD and had been abstinent for at least 5 years. A resource group of peer consultants in long-term recovery from SUDs contributed to the study's planning, preparation, and initial analyses. Participants recalled harmful consequences and significant events during their years of substance use. Pressure and concern from close family members were important in their initial efforts to abstain from substance use. Being able to imagine a different life, and the awareness of existing treatment options, promoted hope and further reinforced their motivation to quit. Greater focus on why those with SUDs want to quit may help direct treatment matching; treatment completion may be more likely if the person's reasons for seeking help are addressed.


Play Therapy: Practitioners' Perspectives on Implementation and Effectiveness

The purpose of the present research was to explore practitioners’ perspectives on play therapy as an intervention when working with a child who has experienced trauma, has present PTSD symptoms and has a co-morbid mental health diagnosis. Play therapy has been accepted as an effective intervention to utilize with children who have been exposed to trauma (Schaefer, 1994). However, there is currently limited research evaluating play therapy as an intervention with children who have been traumatized and have developed PTSD or other mental health symptoms/disorders. The current study aimed to supplement the gap in existing research. Two agencies that serve early childhood mental health clients agreed to participate in the present study by completing an online survey. Data was gathered from 22 practitioner respondents. The results indicate that practitioners believe that play therapy is an effective intervention when treating children with trauma histories, PTSD symptoms, and mental health disorders. The results of the present research support findings from previous literature regarding play therapy when used as an intervention for treating trauma and/or mental health disorders. Furthermore, the present research confirms the notion that creating a safe space for their clients using play therapy is an important part of the intervention process. Given the gap in research surrounding play therapy as an intervention when PTSD and a co-morbid mental health disorders occur concurrently, further research would be beneficial to the field of social work and would positively inform the practitioners who work in early intervention settings. PRACTITIONERS’
  PERSPECTIVES 2


A Reliability Perspective of the Smart Grid

Increasing complexity of power grids, growing demand, and requirement for greater reliability, security and efficiency as well as environmental and energy sustainability concerns continue to highlight the need for a quantum leap in harnessing communication and information technologies. This leap toward a ¿smarter¿ grid is widely referred to as ¿smart grid.¿ A framework for cohesive integration of these technologies facilitates convergence of acutely needed standards, and implementation of necessary analytical capabilities. This paper critically reviews the reliability impacts of major smart grid resources such as renewables, demand response, and storage. We observe that an ideal mix of these resources leads to a flatter net demand that eventually accentuates reliability challenges further. A gridwide IT architectural framework is presented to meet these challenges while facilitating modern cybersecurity measures. This architecture supports a multitude of geographically and temporally coordinated hierarchical monitoring and control actions over time scales from milliseconds and up.


An Approach to Generate Topic Similar Document by Seed Extraction-Based SeqGAN Training for Bait Document

In recent years, topic similar document generation has drawn more and more attention in both academia and industry. Especially, bait document generation is very important for security. For more-like and fast bait document generation, we proposed the topic similar document generation model based on SeqGAN model (TSDG-SeqGAN). In the training phrase, we used jieba word segmentation tool for training text to greatly reduce the training time. In the generation phrase, we extract keywords and key sentence from the subject document as seeds, and then enter the seeds into the trained generation network. Next, we get keyword-based documents and documents based on key sentences from generation network. Finally, we output documents that are most similar to the subject document as the final result. Experiments show the effectiveness of our model.


NB-IoT Technology Overview and Experience from Cloud-RAN Implementation

The 3GPP has introduced a new narrowband radio technology called narrowband Internet of Things (NB-IoT) in Release 13. NB-IoT was designed to support very low power consumption and low-cost devices in extreme coverage conditions. NB-IoT operates in very small bandwidth and will provide connectivity to a large number of low-data-rate devices. This article highlights some of the key features introduced in NB-IoT and presents performance results from real-life experiments. The experiments were carried out using an early-standard-compliant prototype based on a software defined radio partial implementation of NB-IoT that runs on a desktop computer connected to the network. It is found that a cloud radio access network is a good candidate for NB-IoT implementation.


RGB-H-CbCr skin colour model for human face detection

While the RGB, HSV and YUV (YCbCr) are standard models used in various colour imaging applications, not all of their information are necessary to classify skin colour. This paper presents a novel skin colour model, RGB-H-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin colour distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin colour and background environment. The results are also comparable to that of the AdaBoost face classifier.


Multilabel SVM active learning for image classification

Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.


Unhealthy region of citrus leaf detection using image processing techniques

Producing agricultural products are difficult task as the plant comes to an attack from various micro-organisms, pests and bacterial diseases. The symptoms of the attacks are generally distinguished through the leaves, steams or fruit inspection. The present paper discusses the image processing techniques used in performing early detection of plant diseases through leaf features inspection. The objective of this work is to implement image analysis and classification techniques for extraction and classification of leaf diseases. Leaf image is captured and then processed to determine the status of each plant. Proposed framework is model into four parts image preprocessing including RGB to different color space conversion, image enhancement; segment the region of interest using K-mean clustering for statistical usage to determine the defect and severity areas of plant leaves, feature extraction and classification. texture feature extraction using statistical GLCM and color feature by means of mean values. Finally classification achieved using SVM. This technique will ensure that chemicals only applied when plant leaves are detected to be effected with the disease.


New step-up DC-DC converters for PV power generation systems

This paper proposes new step-up DC-DC converters with high ratio capability. The proposed DC-DC converters are derived using combination of boost converters and buck-boost converters. A high ratio capability is achieved by parallel input and series output combination so that the efficiency is better than the one that is achieved by using a conventional boost converter. A method to reduce the input and output ripples are also proposed in this paper. Simulated and experimental results are included to show the validity of the proposed converters.


Multi-document Summarization Using Support Vector Regression

Most multi-document summarization systems follow the extractive framework based on various features. While more and more sophisticated features are designed, the reasonable combination of features becomes a challenge. Usually the features are combined by a linear function whose weights are tuned manually. In this task, Support Vector Regression (SVR) model is used for automatically combining the features and scoring the sentences. Two important problems are inevitably involved. The first one is how to acquire the training data. Several automatic generation methods are introduced based on the standard reference summaries generated by human. Another indispensable problem in SVR application is feature selection, where various features will be picked out and combined into different feature sets to be tested. With the aid of DUC 2005 and 2006 data sets, comprehensive experiments are conducted with consideration of various SVR kernels and feature sets. Then the trained SVR model is used in the main task of DUC 2007 to get the extractive summaries.


A smooth-walled spline-profile horn as an alternative to the corrugated horn for wide band millimeter-wave applications

At millimeter-wave frequencies, corrugated horns can be difficult and expensive to manufacture. As an alternative we present here the results of a theoretical and measurement study of a smooth-walled spline-profile horn for specific application in the 80-120 GHz band. While about 50% longer than its corrugated counterpart, the smooth-walled horn is shown to give improved performance across the band as well as being much easier to manufacture.


An Analysis and Comparison of CDN-P2P-hybrid Content Delivery System and Model

In order to fully utilize the stable edge transmission capability of CDN and the scalable last-mile transmission capability of P2P, while at the same time avoiding ISP-unfriendly policies and unlimited usage of P2P delivery, some researches have begun focusing on CDN-P2P-hybrid architecture and ISP-friendly P2P content delivery technology in recent years. In this paper, we first survey CDN-P2P-hybrid architecture technology, including current industry efforts and academic efforts in this field. Second, we make comparisons between CDN and P2P. And then we explore and analyze main issues, including overlay route hybrid issues, and playing buffer hybrid issues. After that we focus on CDN-P2P-hybrid model analysis and design, we compare the tightlycoupled hybrid model with the loosely-coupled hybrid model, and we propose that there are some main common models which need further study. At last, we analyze the prospective research direction and propose our future work. KeywordsCDN, P2P, P2P Streaming, CDN-P2P-hybrid Architecture, Live Streaming, VoD Streaming Note: This work is supported by 2009 National Science Foundation of China (60903164): Research on Model and Algorithm of New-Generation Controllable, Trustworthy, Network-friendly CDN-P2P hybrid Content Delivery.


Optimality principles in sensorimotor control

The sensorimotor system is a product of evolution, development, learning and adaptation—which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.


New Developments in Space Syntax Software

The Spatial Positioning tool (SPOT) is an isovist-based spatial analysis software, and is written in Java working as a stand-alone program. SPOT differs from regular Space syntax software as it can produce integration graphs and intervisibility graphs from a selection of positions. The concept of the software originates from a series of field studies on building interiors highly influenced by organizations and social groups. We have developed SPOT as a prototype. Basic SPOT operations use selections of positions and creations of isovist sets. The sets are color-coded and layered; the layers can be activated and visible by being turned on or off. At this point, there are two graphs produced in SPOT, the isovist overlap graph that shows intervisibility between overlapping isovist fields and the network integration analysis built on visibility relations. The program aims to be used as a fast and interactive sketch tool as well as a precise analysis tool. Data, images, and diagrams can be exported for use in conjunction with other CAD or illustration programs. The first stage of development is to have a functioning prototype with the implementation of all the basic algorithms and a minimal basic functionality in respect to user interaction.


A collaborative filtering approach to ad recommendation using the query-ad click graph

Search engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we address the sponsored search retrieval problem which is to find and rank relevant ads to a search query. We propose a new technique to determine the relevance of an ad document for a search query using click-through data. The method builds on a collaborative filtering approach to discover new ads related to a query using a click graph. It is implemented on a graph with several million edges and scales to larger sizes easily. The proposed method is compared to three different baselines that are state-of-the-art for a commercial search engine. Evaluations on editorial data indicate that the model discovers many new ads not retrieved by the baseline methods. The ads from the new approach are on average of better quality than the baselines.


Example-Based Methods for Estimating 3 D Human Pose from Silhouette Image using Approximate Chamfer Distance and Kernel Subspace


Toward More Efficient NoC Arbitration : A Deep Reinforcement Learning Approach

The network on-chip (NoC) is a critical resource shared by various on-chip components. An efficient NoC arbitration policy is crucial in providing global fairness and improving system performance. In this preliminary work, we demonstrate an idea of utilizing deep reinforcement learning to guide the design of more efficient NoC arbitration policies. We relate arbitration to a self-learning decision making process. Results show that the deep reinforcement learning approach can effectively reduce packet latency and has potential for identifying interesting features that could be utilized in more practical hardware designs.


TCA: An Efficient Two-Mode Meta-Heuristic Algorithm for Combinatorial Test Generation (T)

Covering arrays (CAs) are often used as test suites for combinatorial interaction testing to discover interaction faults of real-world systems. Most real-world systems involve constraints, so improving algorithms for covering array generation (CAG) with constraints is beneficial. Two popular methods for constrained CAG are greedy construction and meta-heuristic search. Recently, a meta-heuristic framework called two-mode local search has shown great success in solving classic NPhard problems. We are interested whether this method is also powerful in solving the constrained CAG problem. This work proposes a two-mode meta-heuristic framework for constrained CAG efficiently and presents a new meta-heuristic algorithm called TCA. Experiments show that TCA significantly outperforms state-of-the-art solvers on 3-way constrained CAG. Further experiments demonstrate that TCA also performs much better than its competitors on 2-way constrained CAG.


Methods and protocols of modern solid phase Peptide synthesis.

The purpose of this article is to delineate strategic considerations and provide practical procedures to enable non-experts to synthesize peptides with a reasonable chance of success. This article is not encyclopedic but rather devoted to the Fmoc/tBu approach of solid phase peptide synthesis (SPPS), which is now the most commonly used methodology for the production of peptides. The principles of SPPS with a review of linkers and supports currently employed are presented. Basic concepts for the different steps of SPPS such as anchoring, deprotection, coupling reaction and cleavage are all discussed along with the possible problem of aggregation and side-reactions. Essential protocols for the synthesis of fully deprotected peptides are presented including resin handling, coupling, capping, Fmoc-deprotection, final cleavage and disulfide bridge formation.


Understanding the Intention of Information Contribution to Online Feedback Systems from Social Exchange and Motivation Crowding Perspectives

The online feedback system (OFS) has been touted to be an effective artifact for electronic word-of-mouth (EWOM). Accumulating sufficient detailed consumption information in the OFS is essential to the success of OFS. Yet, past research has focused on the effects of OFS on building trust and promoting sales and little knowledge about information provision to OFS has been developed. This study attempts to fill this gap by developing and testing a theoretical model to identify the possible antecedents that lead to the intention of consumers' information contribution to OFS. The model employs social exchange theory to identify benefit and cost factors influencing consumer intention, and motivation crowding theory to explore the moderating effects from environmental interventions that are embodied in OFS. Our preliminary results in general provide empirical support for the model. Practical implications are offered to OFS designers for system customization


Paragon: QoS-aware scheduling for heterogeneous datacenters

Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications.
 We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state.
 We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11% and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.


Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations

As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem.
 In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.


Factored recurrent neural network language model in TED lecture transcription

In this study, we extend recurrent neural network-based language models (RNNLMs) by explicitly integrating morphological and syntactic factors (or features). Our proposed RNNLM is called a factored RNNLM that is expected to enhance RNNLMs. A number of experiments are carried out on top of state-of-the-art LVCSR system that show the factored RNNLM improves the performance measured by perplexity and word error rate. In the IWSLT TED test data sets, absolute word error rate reductions over RNNLM and n-gram LM are 0.4∼0.8 points.


Discriminative Multi-View Interactive Image Re-Ranking

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users’ intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.


PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud

Biometric identification has played an important role in achieving user authentication. For efficiency and economic savings, biometric data owners are motivated to outsource the biometric data and identification tasks to a third party, which however introduces potential threats to user’s privacy. In this paper, we propose a new privacy-preserving biometric identification scheme which can release the database owner from heavy computation burden. In the proposed scheme, we design concrete biometric data encryption and matching algorithms, and introduce perturb terms in each biometric data. A thorough analysis indicates that our schemes are secure, and the ultimate scheme offers a high level of privacy protection. In addition, the performance evaluations via extensive simulations demonstrate our schemes’ efficiency. © 2017 Elsevier Inc. All rights reserved.


An Energy-Efficient Architecture for the Internet of Things (IoT)

Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.