Miracle-dz/UniFAR-FTUs huggingface dataset:
External transactions Internal transactions Source code Operation code Code features Account features Classifier Disassemble Other contracts Ponzi schme contracts Application Label Bytecode Collecting ground truth Extracting features Model & Application FIGURE 2 : The Framework of Smart Ponzi Scheme

Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue and to provide reusable research data sets for future research, this study collects real-world samples and proposes an approach to detect Ponzi schemes implemented as smart contracts (i.e., smart Ponzi schemes) on blockchain. Firstly, 200 smart Ponzi schemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereum platform. Then, two kinds of features are extracted from the transaction history and operation codes of the smart contracts. Finally, a classification model is presented to detect smart Ponzi schemes. Extensive experiments show that the proposed model performs better than many traditional classification models and can achieve high accuracy for practical use. By using the proposed approach, we estimate that there are more than 500 smart Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams. INDEX TERMS Blockchain, Smart Contract, Ponzi Schemes, Ethereum, Data mining
Deploying Architectural Support for Software Defect Detection in Future Processors

1 Motivation Recent impressive advances in semiconductor technology are enabling the integration of ever more transistors on a single chip. This trend provides a unique opportunity to enhance processor functionality in areas other than performance, such as in easing software development or enhancing software debugging. Exploring architectural support for software debugging is a research direction of great promise. First, it enables new tradeoffs in the performance, accuracy, and portability of software defect detection tools, possibly allowing the detection of new types of defects under new conditions. Secondly and most importantly, it opens up a new possibility: on-the-fly software defect detection in production runs. This last area has been under-explored due to the typically large overheads of many software solutions. Table 1 briefly summarizes the architectural supports for software defect detection that our research group has recently proposed.
Intelligent System for Workforce Classification

We examine several machine learning algorithms for assigning job-categories to a large employee population based on their online directory profiles and reporting structures. This application is an important step in workforce management, and also represents a challenging classification problem which typically contains large-scale unbalanced data, i.e., the distribution of job categories is highly skewed. It also requires combining local information, e.g., the textual description in an employee’s webpage, with global structure, e.g., the reporting chain structure, in order to predict job categories. We compare the performance of graphical models to several classification and relational learning algorithms with well-tuned features and show satisfactory accuracies on two datasets of more than 20,000 IBM employees. We also address some challenges that the graphical models face when applied to large-scale data.
Towards scalable and accurate item-oriented recommendations

Most recommenders research aims at personalized systems, which suggest items based on user profiles. However, in reality many systems deal with item-oriented recommendations. In such setups, given a single item of interest, the system needs to provide other related items, following patterns like "people who liked this also liked. ". While item-oriented systems are central in their importance, they have been approached so far using very basic tools. We identify several hurdles faced by standard approaches to the item-oriented task. First, the sparseness of observed activities prevents establishing reliable similarity relations for many item pairs. Second, we address a scalability challenge at the retrieval stage present in many real-world systems: Given an item inventory, which may encompass millions of items, it is desired to identify the most related item pairs in a sub-quadratic time. This work addresses these two challenges, thereby improving both accuracy and scalability of item-oriented recommenders. Additionally, we propose an empirical evaluation scheme for comparing the quality of different solutions with encouraging results.
Approximation Methods for Thin Plate Spline Mappings and Principal Warps

The thin plate spline (TPS) is an effective tool for modeling coordinate transformations that has been applied successfully in several computer vision applications. Unfortunately the solution requires the inversion of a p × p matrix, where p is the number of points in the data set, thus making it impractical for large scale applications. In practical applications, however, a surprisingly good approximate solution is often possible using only a small subset of corresponding points. We begin by discussing the obvious approach of using this subset to estimate a transformation that is then applied to all the points, and we show the drawbacks of this method. We then proceed to borrow a technique from the machine learning community for function approximation using radial basis functions (RBFs) and adapt it to the task at hand. Using this method, we demonstrate a significant improvement over the naive method. One drawback of this method, however, is that is does not allow for principal warp analysis, a technique for studying shape deformations introduced by Bookstein based on the eigenvectors of the p× p bending energy matrix. To address this, we describe a third approximation method based on a classic matrix completion technique that allows for principal warp analysis as a by-product. By means of experiments on real and synthetic data, we demonstrate the pros and cons of these different approximations so as to allow the reader to make an informed decision suited to his or her application.
Document Fusion for Comprehensive Event Description

This paper describes a fully implemented system for fusing related news stories into a single comprehensive description of an event. The basic components and the underlying algorithm are explained. The system uses a computationally feasible and robust notion of entailment for comparing information stemming from different documents. We discuss the issue of evaluating document fusion and provide some preliminary results.
RTC GENE DELETION LEADS TO INCREASED SUSCEPTIBILITY TO HEPATOCELLULAR CARCINOGENESIS

Objective: to study the relationship between retuculon C (RTC) gene deletion and susceptibility to hepatocellular carcinoma (HCC) in vivo. Methods and Results: Western blot analysis for cell cycle-associated proteins showed that the levels of expression of cyclin D, Cdk 2 and 4, and cdcA were not altered in the hepatocytes of RTC heterozygous (HG) mice when compared with those of wild-genotype (WG) littermates. On the other hand, p27 and c-myc expression levels were significantly decreased in liver tissue of 20 days-old HG mice and unaffected — in 10 months-old animals; cyclin A expression was unaffected in liver tissue of 20 days-old HG mice but increased — in 10 months-old ones. The level of RTC protein expression was significantly lower in HG tumors in comparison with surrounding normal hepatocytes. HG mice had significantly increased incidence of spontaneous and chemically induced HCC compared to WG littermates. Conclusions: our data suggest that the decreased level of RTC expression results in higher susceptibility to hepatocellular carcinoma.
hnRNPL and nucleolin bind LINE-1 RNA and function as host factors to modulate retrotransposition

Long INterspersed Element one (LINE-1, or L1), is a widely distributed, autonomous retrotransposon in mammalian genomes. During retrotransposition, L1 RNA functions first as a dicistronic mRNA and then as a template for cDNA synthesis. Previously, we defined internal ribosome entry sequences (IRESs) upstream of both ORFs (ORF1 and ORF2) in the dicistronic mRNA encoded by mouse L1. Here, RNA affinity chromatography was used to isolate cellular proteins that bind these regions of L1 RNA. Four proteins, the heterogeneous nuclear ribonucleoproteins (hnRNPs) R, Q and L, and nucleolin (NCL), appeared to interact specifically with the ORF2 IRES. These were depleted from HeLa cells to examine their effects on L1 IRES-mediated translation and L1 retrotransposition. NCL knockdown specifically reduced the ORF2 IRES activity, L1 and L1-assisted Alu retrotransposition without altering L1 RNA or protein abundance. These findings are consistent with NCL acting as an IRES trans-acting factor (ITAF) for ORF2 translation and hence a positive host factor for L1 retrotransposition. In contrast, hnRNPL knockdown dramatically increased L1 retrotransposition as well as L1 RNA and ORF1 protein, indicating that this cellular protein normally interferes with retrotransposition. Thus, hnRNPL joins a small, but growing list of cellular proteins that are potent negative regulators of L1 retrotransposition.
RefDistiller: a refactoring aware code review tool for inspecting manual refactoring edits

Manual refactoring edits are error prone, as refactoring requires developers to coordinate related transformations and understand the complex inter-relationship between affected types, methods, and variables. We present RefDistiller, a refactoring-aware code review tool that can help developers detect potential behavioral changes in manual refactoring edits. It first detects the types and locations of refactoring edits by comparing two program versions. Based on the reconstructed refactoring information, it then detects potential anomalies in refactoring edits using two techniques: (1) a template-based checker for detecting missing edits and (2) a refactoring separator for detecting extra edits that may change a program's behavior. By helping developers be aware of deviations from pure refactoring edits, RefDistiller can help developers have high confidence about the correctness of manual refactoring edits. RefDistiller is available as an Eclipse plug-in at https://sites.google. com/site/refdistiller/ and its demonstration video is available at http://youtu.be/0Iseoc5HRpU.
Detecting malicious ADS-B broadcasts using wide area multilateration

Automatic Dependent Surveillance Broadcast (ADS-B) is an emerging radio technology capable of extending or replacing current RADAR-based surveillance for air traffic management. However, due to its reliance on unencrypted radio transmissions, ADS-B is exploitable using relatively inexpensive and open source tools. This paper demonstrates how multilateration technique can be applied to detect some types of malicious ADS-B transmissions, based on known GPS errors (used by ADS-B transmitters) and clock precision inaccuracies (used by multilateration systems). As it requires the measurement of radio signal's time of arrival (TOA) at different sensors to calculate the transmitter's location, the placement of those sensors play an important role to determine the accuracy of the estimates. In this direction, this paper also presents a method based on genetic algorithms in order to determine the position of sensors that gives improved coverage and reduced error.
International Journal of Women's Health Dovepress a Critical Appraisal of the Misoprostol Removable, Controlled-release Vaginal Delivery System of Labor Induction

Background: Induction of labor is a major issue in pregnancy management. Finding strategies to increase rate and decrease time to vaginal delivery is an important goal, but maternal or neonatal safety must remain the primary objective. Misoprostol is a synthetic analogue of prostaglandin used off label to ripen the cervix and induce labor. The misoprostol vaginal insert (MVI) was designed to allow a controlled-release delivery of misoprostol (from 50 to 200 µg) with a removal tape. The objective of this review was to make a critical appraisal of this device referring to the literature. Methods: A literature search was performed in the PubMed and Cochrane databases using the keywords " vaginal misoprostol insert ". Results: Several studies compared different doses of MVI (50, 100, 150, and 200 µg) with the 10 mg dinoprostone insert. The 100 µg MVI compared with the dinoprostone vaginal insert (DVI) showed similar efficacy and no significant differences in cesarean delivery rate. MVI 200 µg compared with DVI showed a reduced time to vaginal delivery and oxytocin need but had an increased risk of uterine hyperstimulation. The rate of hyperstimulation syndrome was two to three times more frequent with the 200 µg MVI than the 100 µg. Conclusion: Current data suggest that the 100 µg MVI would provide the best balance between efficacy and safety. Further studies should be performed to evaluate this dose, especially in high-risk situations needing induction of labor.
User modelling for personalised dressing assistance by humanoid robots

Assistive robots can improve the well-being of disabled or frail human users by reducing the burden that activities of daily living impose on them. To enable personalised assistance, such robots benefit from building a user-specific model, so that the assistance is customised to the particular set of user abilities. In this paper, we present an end-to-end approach for home-environment assistive humanoid robots to provide personalised assistance through a dressing application for users who have upper-body movement limitations. We use randomised decision forests to estimate the upper-body pose of users captured by a top-view depth camera, and model the movement space of upper-body joints using Gaussian mixture models. The movement space of each upper-body joint consists of regions with different reaching capabilities. We propose a method which is based on real-time upper-body pose and user models to plan robot motions for assistive dressing. We validate each part of our approach and test the whole system, allowing a Baxter humanoid robot to assist human to wear a sleeveless jacket.
Gibsonian Modeling of Users in Social Networks

In recent days, social networks are creating huge amounts of data that need to be managed in intelligent ways. Ontologies always play important roles in these contexts: users and social objects may refer to concepts and this can provide efficient methods to store, retrieve and recommend contents. Everything, however, relies on the concept of interaction.
Challenging the dual assumption of the ‘always/already’ autonomous student and effective supervisor

Postgraduate supervision, until recently, was regarded as an extension of research rather than as a form of teaching. Research students were assumed to be ‘always/already’ autonomous scholars at the beginning of their candidature. So too, postgraduate supervisors were assumed to be ‘always/already’ effective at supervising once they had endured the process themselves. Currently, postgraduate supervision is regarded as a form of mentoring, where students gradually master appropriate disciplinary research knowledge. Yet, supervisors also wrestle with the contradictory role of disciplinary gatekeeper. As a result, the ‘always/already’ autonomous student and effective supervisor pair remains a strong underlying assumption in supervision pedagogy. This article explores how Justine, a new supervisor in the Health Sciences, and Catherine, an academic developer and supervisor in the Humanities/Higher Education, hope to contribute to this debate by developing a collaborative approach to enhancing research students’ critical analysis and independent learning abilities.
TOSThreads: Extending the TinyOS Concurrency Model to Support Preemption

We present TOSThreads, an application-level threads package for TinyOS. In contrast to previous threads packages proposed for TinyOS, TOSThreads supports fully preemptive threads while maintaining the existing TinyOS concurrency model. TOSThreads deﬁnes a conceptual user/kernel boundary, whereby TinyOS runs as a high-priority kernel thread and application threads execute at lower priority. This approach naturally supports long-running computations while preserving the timing-sensitive nature of TinyOS itself. Additionally, the existence of a user/kernel boundary enables dynamic linking and loading of application binaries at runtime. The API at this boundary deﬁnes the set of TinyOS services supported by a TOSThreads kernel and is customizable in support of a diverse set of applications. We demonstrate that TOSThreads context switches and system calls introduce an overhead of less than 0.92% and that dynamic linking and loading takes as little as 90ms for a representative sensing application. We compare diﬀerent programming models built on top of TOSThreads, including standard C with blocking sys-tem calls and a reimplementation of Tenet. Additionally, we evaluate the ability of TOSThreads to run computationally intensive tasks. Taken as a whole, these results suggest that TOSThreads is an important step towards deﬁning a TinyOS kernel that can support long-running computations and high concurrency.
Correlation based direction of arrival estimation for the large scale multi-user MIMO system

It is well known that accurate direction of arrival (DOA) estimation can help to improve the performance of MIMO systems. However, when large scale MIMO systems are considered, the DOA estimation becomes very difficult due to the large dimension of DOAs. Based on the observation that correlation exists between the neighboring antennas due to the limited size of the antenna array, we propose a correlation based DOA estimation method for the large scale multi-user (MU) MIMO system in this paper. It is verified that the proposed DOA estimation method can obtain a better performance than the existing methods with a much lower computational complexity.
Malleable Games - A Literature Review on Communities of Game Modders

The creations of people who modify games (game "modders") have had an significant impact on economics of game development. Being part of a creative participatory culture, their mods serve as a source of inspiration that has even steered the direction of game development in the past. This paper presents the findings of a comprehensive literature review on game modding. We structure our findings according to the three perspectives from which game modding has been analyzed: an outside perspective, a meta-level, and an inside perspective. We identify aspects that have received little academic attention so far, but warrant future research. Our analysis of the body of literature shows that only few publications focus more closely on community aspects such as differences among individual game modding communities, dynamics and collaboration within modder communities. We argue that gaining a deeper understanding of these aspects is essential in order to be able to capitalize from the huge creative potential that lies within game modding communities.
The Effects of Altercasting and Counterattitudinal Behavior on Compliance: A Lost Letter Technique Investigation

A field experiment was conducted to test the effectiveness of altercasting (Weinstein & Dutschberger, 1963) as a compliance-gaining technique. The central hypothesis predicts that positive altercasting messages should produce greater compliance than direct requests. Following Milgram's (1969) lost letter technique, 2,400 ostensibly “lost” letters were placed on car windshields throughout a metropolitan area along with a business card containing a handwritten altercasting or direct request message to mail the letter. The frequency of letters returned was used as a primary measure of compliance. The results do not demonstrate the effectiveness of altercasting as a compliance-gaining technique; on the contrary, they indicate that negative altercasting significantly reduces compliance.
Types of embedded graphs and their Tutte polynomials

Abstract We take an elementary and systematic approach to the problem of extending the Tutte polynomial to the setting of embedded graphs. Four notions of embedded graphs arise naturally when considering deletion and contraction operations on graphs on surfaces. We give a description of each class in terms of coloured ribbon graphs. We then identify a universal deletion-contraction invariant (i.e., a ‘Tutte polynomial’) for each class. We relate these to graph polynomials in the literature, including the Bollobás–Riordan, Krushkal and Las Vergnas polynomials, and give state-sum formulations, duality relations, deleton-contraction relations, and quasi-tree expansions for each of them.
A batch scheduler with high level components

In this article we present the design choices and the evaluation of a batch scheduler for large clusters, named OAR. This batch scheduler is based upon an original design that emphasizes on low software complexity by using high level tools. The global architecture is built upon the scripting language Perl and the relational database engine Mysql. The goal of the project OAR is to prove that it is possible today to build a complex system for resource management using such tools without sacrificing efficiency and scalability. Currently, our system offers most of the important features implemented by other batch schedulers such as priority scheduling (by queues), reservations, backfilling and some global computing support. Despite the use of high level tools, our experiments show that our system has performances close to other systems. Furthermore, OAR is currently exploited for the management of 700 nodes (a metropolitan grid) and has shown good efficiency and robustness.
ASAM KLORIDA THE UTILIZATION OF ULTRASOUND-ASSISTEDEXTRACTION OF PECTIN FROM BANANA PEELS WITH CHLORIDE ACID SOLVENT

The Banana is the most widely produced fruit in Indonesia with waste of banana peel about 1/3 of the fruit. If the banana peel is not utilized in large quantities it will cause environmental problems and is a waste of resources because the waste can still be utilized. The Banana peel contains a variety of compounds such as pectin 10-21%. Based on its content, banana peels have potential as a source of pectin. Pectin can be utilized in various industries of making jelly, jam, gelling, thickener, stabilizer and emulsifier. The purpose of this study was to find the influence of ultrasonic and mass on pectin yield. This research was conducted by using ultrasonic method extraction using 0.05 N HCl solvent of 400 mL and extraction temperature of 60 C. variationof mass in this research are 5, 10, 20, 30 and 40 grams. The highest mass obtained in ultrasonic method is 20.0008 gram with