Miracle-dz/UniFAR-FTUs huggingface dataset:
Image segmentation for defect detection based on level set active contour combined with saliency map

In this paper we propose a new image segmentation method to detect surface defects with small shapes in images characterized by low contrast level and affected by non-uniform illumination. The proposed segmentation method is based on level set active contour approach where a center-surround feature saliency map used to suppress the clutter background and highlighting the potential objects is incorporated in the level set active contour formulation. Experimental results on different industrial images like welding and steel demonstrate the robustness and the accuracy in defect segmentation comparing with others active contour models.
Five years after Carmody: practitioners’ views of changes, challenges and research in child protection

Abstract Child protection work is a complex and difficult area of practice, one that is closely scrutinised and criticised, and impacts on the lives of many children. In Australia, child protection systems are overloaded, and increasing numbers of children and families receive child protection interventions each year. This study explored the views of North Queensland practitioners who work in the child protection field, examining changes and challenges in this field of practice, and their suggestions for the future research that is needed in child protection. The study took place 5 years after the 2013 Queensland Carmody inquiry into child protection intervention, which recommended sweeping changes to the child protection system. Twenty-two practitioners participated in this study. Respondents reported an increase in the complexity of cases, a gap in legislation change/practice frameworks and practice, and the application of trauma-informed practice. They highlighted the intersection of child protection, domestic violence and family law and observed that women and children continue to be exposed to violence because of Family Law Court orders. Respondents identified a number of areas where research is needed.
Activity correlation-based clustering clock-gating technique for digital filters

ABSTRACT Clock gating is an effective way to reduce the dynamic power in digital sequential circuits. In this paper, a gate-level activity correlation-based clustering clock-gating (CCG) technique is proposed for digital filters. The CCG technique exploits the correlations between flip-flops, and determines how to group the flip-flops for clock gating. An Activity Correlation Matrix (ACMtx) is introduced to describe the correlations between the flip-flops, and a greedy clustering algorithm is proposed to find an optimised clustering scheme as well. Experiments on ISCAS’89 benchmarks show that the proposed technique can reduce power consumption by 5.08% on average, on top of existing technique. For the circuits with large numbers of flip-flops, our proposed technique can save 15.84% more power on average.
Micro-blog topic detection method based on Latent Semantic Analysis

As the large popularity of micro-blog and awareness continues to improve, hot topic of micro-blog detecting has become the current research focuses. For short texts, there exist high-dimension, sparse, synonymy and polysemy problems for Vector Space Mode(lVSM)text presentation, making it difficult to measure the similarity of the texts accurately. This paper presents a two-stage cluster based on Latent Semantic Analysis(LSA)topic detection approach. Firstly, the concept of hot topic is introduced to select micro-blogs with certain attention, using LSA to model the dataset. Then CURE algorithm of hierarchical clustering is employed to determine the initial centers. Finally, the hot topic clustering results are obtained through K-means clustering. Experimental results on real micro-blog dataset verify the validity of the method.
Chinese venturers to Pacific Small Island Developing States: Travel and lifestyle

This study emphasizes the necessity to understand the needs of the growing Chinese market, while also assessing how ready Small Island Developing States (SIDS) of the South Pacific are for these new visitors. Through a mixed method approach, combining individual visitor surveys and focus groups, this study investigates the lifestyle characteristics of outbound Chinese tourists to the Cook Islands and the motivation and travel behavior of these travelers. We refine a model for the travel lifestyle of Chinese tourists that has applicability to SIDS elsewhere around the world.
Osteogenesis of Umbilical Mesenchymal Stem Cells in Photo-crosslinked Poly(ethylene glycol)/ε-caprolactone Hydrogels

Poly (ethylene glycol) (PEG) was copolymerized with E-caprolactone and then acrylated to produce photo-crosslinked cell encapsulation hydrogels. The osteogenesis of encapsulated umbilical mesenchymal stem cells (UMSCs) was induced in these hydrogels and PEG hydrogels for comparison. The physical properties of the hydrogel, including the swelling ratio and compressive modulus, were measured. Gel degradation in phosphate buffered saline was also tested. Changes in cell morphology in the hydrogel were examined. Collagen type I gene expression, alkaline phosphatase activity, and calcium deposition were used to evaluate the degree of osteogenesis in the hydrogels. It was found that hydrogels composed of PEG copolymerized with e-caprolactone are suitable for inducing osteogenesis of UMSCs and thus helpful for bone regeneration.
Validity and reliability of the questionnaire that evaluates factors associated with perceived environmental behavior and perceived ecological purchasing behavior in Peruvian consumers

Purpose The purpose of this study is to determine the metric properties of a questionnaire that evaluates environmental and ecological purchasing behavior, environmental personal norms, environmental identity and environmental social influence. Design/methodology/approach The validity and reliability of the scales of the questionnaires were determined in a sample of 2445 consumers from Lima (Peru), selected through non-probabilistic sampling by quotas and by factorial analysis based on Classical Theory of Tests (CTT) and Structural Equations of Variance with Partial Least Squares (SEM-PLS). Findings The results support the construct and discriminant validity of the instrument, as well as the internal consistency of all the subscales (Cronbach’s alpha between 0.662 and 0.8887 – composite reliability between 0.815 and 0.917). Research limitations/implications Customers evaluated were only from Lima city. It would be important to evaluate, in future research, customers from other cities in Peru. Practical implications Although a large number of instruments have been designed, not all are based on integral theoretical models, and their metric properties were determined with methodological criteria that restrict their applicability. Therefore, there is a need to have valid and reliable instruments for the identification of environmental behavior and ecological purchasing. Social implications This “new questionnaire” integrates the exploration of environmental behavior and ecological purchasing, along with the measurement of personal environmental norms, environmental identity and the social influence received from peers and teachers, to provide a comprehensive picture of the consumer behavior. Originality/value This research constitutes a theoretical and practical contribution to the understanding and evaluation of ecological behavior and some of its associated factors. Its main contribution is the adaptation of this instrument to the Peruvian context and the validation of an instrument that evaluates environmental and ecological purchasing behavior, personal environmental standards, environmental identity and environmental social influence.
Stock Returns and Asymmetric Volatility Spillover Dynamics Between Asian Emerging Markets

This study investigates the dynamics of volatility spillover among Asian emerging stock markets over the period from 1 January 2002 to 29 December 2017. This study applies extended EGARCH model to estimate the asymmetric volatility spillovers. The findings of this study are interesting. This study finds statistically significant own past volatility spillovers in all selected stock markets. We find bidirectional significant spillovers of volatility in most of the selected markets. Moreover, we find significant asymmetric volatility spillover in all case of stock markets. Furthermore, the findings reveal statistically insignificant volatility spillover from China to India, China to Indonesia, China to Pakistan, Pakistan to China, Pakistan to Indonesia, Pakistan to Korea and Pakistan to Taiwan in this study period. The knowledge of return linkages and volatility spillover amongst Asian emerging financial markets has great implications for global investors, portfolio managers and policymakers.
A Survey on Leaf Disease Prediction Algorithms using Digital Image Processing

Objective : To investigate the plant leaf disease prediction algorithms that utilizes the digital image processing techniques in agricultural environments. Findings : In digital image processing, the segmentation process of healthy and diseased tissue was mainly focused in order to detect and diagnose the plant leaf diseases accurately.Semi-automatic segmentation technique was mostly utilized among various segmentation methods, which was developed based on the grayscale histogram. However, the issue of accuracy in segmentation process was still not improved. In this paper, the leaf disease prediction algorithms are investigated briefly according to the digital image processing techniques and evaluated the performance effectiveness of different algorithms. Results : In this paper, various segmentation algorithms are studied which are used to predict the leaf diseases through digital image processing techniques in terms of their merits and demerits to prove segmentation based on grayscale histogram is better than other segmentation techniques to predict the leaf diseases. Application/Improvements : The finding of this study shows that the segmentation technique based on grayscale histogram is better than the other digital image processing techniques.
Explore the method of technology evolution research based on patents network

Technology evolution research can make the developing process,history and status of a specific field clear.It's valuable for government and enterprises' ST strategy management.Patent citation analysis has some disadvantages such as it can't judge the technology similarity of two patents accurately and the scope of analysis and richness of potential information is limit in technology evolution research.Text mining method can analyze text of patents deeply.It can make up the disadvantages of patent citation analysis to some degree.So we explore the combination of patent citation analysis and text mining method to research technology evolution.We construct C-T(Citation-Text) patent network based on patent citation matrix and patent text similarity matrix,then we do a cluster analysis and visualize the results.This paper aims to carry out the innovation of technology evolution research methods and enrich methodology of technology evolution research.
Reversible Color Transformation: Method To Secure Secret Image By Transforming Into Secret Fragment Visible Mosaic Image

Security of informat ion is becoming main target for modern network age. Internet is the most popular medium for transferring of data. The images which are being transmitted are have various applications, such as confidential enterprise archives, etc. As these images contain secret information which have to protect from unauthorized receptors during transmission of these images hence, a new secure transmission technique is proposed, by transforming confidential or secret image into meaningful secret fragment mosaic image. This mosaic tile image is almost of same size and looks similar to the selected target image. The proposed method contains technique which forms the secret-fragment-visib le mosaic image using reversible color transformat ion scheme. The blocks fragment of secret image are arrange in different ways to produce mosaic image which is seems to be different from secret image. The color transformation process is used, so that secret image may be recovered nearly losslessly. A key is embedded by a losslessly data hiding scheme into created mosaic image for recovery of the original secret image.
Three Essays on Market Imperfections and Inequality

Author(s): Griffy, Benjamin Sherlock | Advisor(s): Rupert, Peter | Abstract: My dissertation focuses on margins that may disproportionally impact impoverished individuals. I primarily focus on two imperfections that I show contribute to inequality: frictional labor markets, and imperfect credit markets. Understanding the consequences of each, as well as how they interact, is central to better understanding the sources of inequality.My first chapter quantifies the impact of borrowing constraints on consumption and earnings inequality using a life-cycle model. I first show that following an unemployment spell, likely-constrained workers in the Survey of Income and Program Participation match to jobs that pay more per quarter when they receive an increase in their unemployment insurance. I then construct a life-cycle model with risk averse workers who face borrowing constraints, accumulate human capital endogenously, and search both on and off the job. I use indirect inference to estimate the model parameters, and show that wealth inequality causes both placement into lower-paying jobs as well as slower human capital accumulation when workers face borrowing constraints. Unemployment risk is partially responsible for this change in human capital accumulation. I compare changes in initial conditions and find that a standard deviation decrease in initial wealth causes a larger decline in life-cycle consumption than a standard deviation decrease in initial human capital.My second chapter deals with the appropriate approach to modeling frictional labor markets, and is joint work with Christine Braun, Bryan Engelhardt, and Peter Rupert. In it, we address whether the arrival rate of a job independent of the wage that it pays. To do this we address how, and to what extent, unemployment insurance changes the hazard rate of leaving unemployment across the wage distribution using a Mixed Proportional Hazard Competing Risk Model and data from the 1997 National Longitudinal Survey of Youth. Controlling for worker characteristics we reject that job arrival rates are independent of the wages offered. We apply the results to several prominent job-search models and interpret how our findings are key to determining the efficacy of unemployment insurance.My final chapter addresses whether public education plays a role in decreasing intergenerational persistence of income. Intuitively, impoverished families face constraints when their children are young: either in moving to better school districts, or in buying adequate supplies for their children. I explore empirically the extent to which increases in public education spending can decrease the importance of a parent's income in determining their child's. I expand on the previous literature by using an instrument for government spending in order to assess the effect of public spending on public education in changing income persistence. I find that an increase in government spending on education significantly decreases persistence of income across generations.
Visual Analytics Towards Big Data

Visual analytics is an important method used in big data analysis. The aim of big data visual analytics is to take advantage of human's cognitive abilities in visualizing information while utilizing computer's capability in automatic analysis. By combining the advantages of both human and computers, along with interactive analysis methods and interaction techniques, big data visual analytics canhelp people to understand the information, knowledge and wisdom behind big data directly and effectively. This article emphasizes on the cognition, visualization and human computer interaction. It first analyzes the basic theories, including cognition theory, information theory, interaction theory and user interface theory. Based on the analysis, the paper discusses the information visualization techniques used in mainstream applications of big data, such as text visualization techniques, network visualization techniques, spatio-temporal visualization techniques and multi-dimensional visualization techniques. In addition, it reviews the interaction techniques supporting visual analytics, including interface metaphors and interaction components, multi-scale/multi-focus/multi-facet interaction techniques, and natural interaction techniques faced on Post-WIMP. Finally, it discusses the bottleneck problems and technical challenges of big data visual analytics.
A HADOOP-Based Framework for Parallel and Distributed Feature Selection

In this paper, we introduce a theoretical basis for a Hadoop-based framework for parallel and distributed feature selection. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of four feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop MapReduce. Hadoop allows parallel and distributed processing so each feature selector can be processed in parallel and multiple feature selectors can be processed together in parallel allowing multiple feature selectors to be compared. We identify commonalities among the four features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all four feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop.
Active asset allocation for retirement funds using the fed model

Active asset allocation, also known as market timing, is controversial but potentially effective for individual investors and financial advisors. Many studies support market timing based on the relationship between the aggregate earnings yield on equities and the intermediate Treasury bond yield, known as the Fed model. Nevertheless, skeptics point to common flaws in these studies and challenge the validity of the Fed model. In general, returns from timing models are difficult to adjust for transaction costs and tax effects from short term gains and losses. In almost all cases, there is data mining from reporting results over the same period used to build the timing model. Our study addresses these concerns directly. We control the transaction costs and tax effects by focusing on funds available for retirement accounts within a Vanguard fund family allowing costless monthly transfers. We use a “risk on” or “risk off” approach rather than experiment with arbitrary cutoff rules for switching funds. We first test for the time series properties of the Fed model to build a prediction model and then apply it over a recent five-year holdout of period. Our findings show that the switching portfolio offers attractive performance compared to either of the Vanguard funds, especially with respect to enhancing upside to downside risk ratios.
Exploring Carry Trade and Exchange Rate toward Sustainable Financial Resources: An application of the Artificial Intelligence UKF Method

This paper constructs a heterogeneous agent model for the foreign exchange market that is based on the law of supply and demand and includes carry trade, central bank intervention, and macroeconomic fundamentals. With the artificial intelligence method of the unscented Kalman filter, this paper investigates carry traders’ expectation formation and risk aversion and the impact of their activities on the movement of the Chinese yuan exchange rate and on the efficiency of central bank intervention. The findings demonstrate that carry traders’ activities are partially responsible for fluctuations in the Chinese yuan exchange rate; carry traders behave with obvious risk aversion; their activities tend to weaken the ability of the central bank to intervene in China’s foreign exchange market; and the volatility of the Chinese yuan exchange rate and the weight of carry traders are negatively related. Based on these empirical results, specific suggestions for exploring sustainable financial resources are provided.
Pre-FOMC Announcement Relief

We show that the pre-FOMC announcement drift in equity returns occurs mostly in periods of high market uncertainty or risk premium. Specifically, this abnormal return is explained by a significant reduction in the risk premium (implied volatility and variance risk premium) prior to the announcement, but only when the risk premium is high, e.g., when it is above its median. Likewise, the magnitude of the FOMC Cycle and other related patterns varies with uncertainty and risk premium. Market uncertainty measures are persistent and are not related to policy uncertainty or expectations. Markets become only marginally stressed in the days prior to the announcement and changes in uncertainty appear to be of lower frequency. We also explain why recent studies suggest that the pre-FOMC drift might have disappeared in the past decade, as this moderation is due to time variation that was also present in older data. Additionally, CAPM only works on FOMC dates when the risk premium is high, e.g., implied vol above its prior median level. The results are robust to different samples and measures of risk premium and uncertainty.
Signal processing for improved MPEG-based communication systems

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
An investigation on the generative mechanisms of Dark Net markets

In this paper we investigate the Dark Net which is the part of Internet accessible only via special browsers such as Tor. The Dark Net is the home of black-markets for illegal goods and services such as drugs, weapons and fake identities. In this study we investigate the Dark Net as a digital infrastructure over time to address the following research question: what are the forces underlying Dark Net markets? Our empirical approach is based on a set of techniques for accessing Dark Net marketplaces (DNM) and collecting various types of information on sites, transactions and users. We draw also on secondary sources such as reports of police interventions and interviews. Our analysis follows the tradition of critical realism to shed light on the generative mechanisms enabling Dark Net markets to operate and survive. 1 Corresponding author. pspagnoletti@luiss.it +39 0685225795 Spagnoletti et al. The generative mechanisms of dark net markets Proceedings of the 13th Pre-ICIS Workshop on Information Security and Privacy, San Francisco, December 13, 2018. 2