Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Method for Utility Preserving Social Network Graph Anonymization Based on Mathematical Modeling

In recent years, privacy concerns about social network graph data publishing has increased due to the widespread use of such data for research purposes. This paper addresses the problem of identity disclosure risk of a node assuming that the adversary identifies one of its immediate neighbors in the published data. The related anonymity level of a graph is formulated and a mathematical model is proposed to solve the problem. The application of the method on a number of synthetic and real-world datasets confirms that the method is general and can be used in different contexts to produce superior results in terms of the utility of the anonymized graph.
Replay Buffer start Strategy a ) Strategy b ) EncoderEncoder Encoder Encoder EncoderGoal Goal DBTaskDemonstrationsif successful Online Goal Selection

Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of “narrow passages” in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern reinforcement learning (RL) due to the associated long-horizon nature of the problem and the lack of sufficient positive signal during learning. Various tools have been applied to address this challenge. When available, large sets of demonstrations can guide agent exploration. Hindsight relabelling on the other hand does not require additional sources of information. However, existing strategies explore based on task-agnostic goal distributions, which can render the solution of long-horizon tasks impractical. In this work, we extend hindsight relabelling mechanisms to guide exploration along task-specific distributions implied by a small set of successful demonstrations. We evaluate the approach on four complex, single and dual arm, robotics manipulation tasks against strong suitable baselines. The method requires far fewer demonstrations to solve all tasks and achieves a significantly higher overall performance as task complexity increases. Finally, we investigate the robustness of the proposed solution with respect to the quality of input representations and the number of demonstrations.
OPINION MINING : APPLICATIONS, TECHNIQUES, TOOLS, CHALLENGES AND FUTURE TRENDS OF SENTIMENT ANALYSIS

: Today’s generation express their views and opinions publicly which can be in favour or against any product, service, issue, event or policy. With the rapid growth of internet, people can give their feedback on the web in large numbers. For any organization or for individuals, this huge amount of feedback is very crucial to improve their products and services. This huge volume of reviews can be analyzed by Opinion Mining (also known as Semantic Analysis). It is an emerging field for researchers which aims to distinguish the emotions expressed within the reviews, classifying them into positive or negative opinions and summarizing it into a form that is easily understood by users. Opinion Mining can be used in the process of information retrieval and web data analysis. It helps the organizations for decision making. The exponential growth of the user generated content has opened new horizons for research in the field of sentiment analysis [1]. This paper presents a review that covers the different techniques and approaches that are used in opinion mining systems. Also, this paper highlights various application areas and challenges related to the Opinion Mining .
Verification of Protocol Design using UML - SMV

Designing a new protocol, which is reliable and meets all the specifications, is a challenging task. There are many instances of protocol failing (when actually deployed) even after thorough testing. To enhance the correctness of a protocol design, formal verification techniques are used. The formal verification is a complementary approach to testing for detecting subtle design errors. In formal verification, the behavior of the protocol is described using a program like notation, collection of finite state machines or temporal logic formulae and then this description is compared with the specification of the desired behavior. If the verifier detects any flaw in the behavioral description, then it generates a counter example or an error trace that illustrates how the problem has occurred [1]. In this paper, we present a pragmatic semiformal approach for verifying the protocol design. We have considered UML statechart diagrams [2] for describing the behavior of the protocol. The desired behavioral property (safety property) is specified using temporal logic formula [3]. The problem associated with the model verification is “State Explosion” [4]. The state space search technique presented in this paper restricts the exploration of number of states, thereby saves the time and the memory required. We have demonstrated the correctness of our approach by verifying the UML statechart
METABANK: A KNOWLEDGE‐BASE OF METAPHORIC LANGUAGE CONVENTIONS

The frequent and conventional use of nonliteral language has been a major stumbling block for natural language processing systems since the early machine translation efforts. Metaphor, metonymy, and indirect speech acts are among the most troublesome phenomena. Recent computational efforts addressing these problems have taken an approach that emphasizes the use of systematic knowledge about nonliteral language conventions. We are currently engaged in an effort to supply this knowledge in the case of conventional metaphor. We are constructing MetaBank: an empirically derived and theoretically motivated knowledge‐base of English metaphorical conventions. This article describes our three‐part approach to the construction of MetaBank: the collection of on‐line textual resources and databases of linguistic generalizations, the development of a methodology for analyzing these resources, and the construction of a knowledge‐base based on the preceding analyses.
Feature point tracking : Robustness to specular highlights and lighting variations

Since the precise modeling of reflection is a difficult task, most feature points trackers assume that objects are lambertian and that no lighting change occurs. To some extent, a few approaches answer these issues by computing an affine photometric model or by achieving a photometric normalization. Through a study based on specular reflection models, we explain explicitly the assumptions on which these techniques are based. Then we propose a tracker that compensates for specular highlights and lighting variations more efficiently when small windows of interest are considered. Experimental results on image sequences prove the robustness and the accuracy of this technique in comparison with the existing trackers. Moreover, the computation time of the tracking is not significantly increased.
Transport requirements for high performance network applications that are NOT FTP

Author(s): Shalf, J.; Bennett, M.J.; Bell, G. | Abstract: The exponential growth of high bandwidth global networks has rapidly exposed the design limitations of the TCP protocol. The congestion avoidance behavior of TCP has been implicated as a likely culprit for poor single-stream TCP throughput on high bandwidth-delay-product networks. The network research community has offered wide range of alternatives to fix TCP's congestion avoidance behavior, but every available technique has focused on high-throughput for bulk flows like file transfers. This paper focuses on the requirements of interactive visualization and control applications that have been neglected in the design of these new protocols.
Hierarchical aggregation for efficient shape extraction

C. Xiao ( ) Computer School, Wuhan University, 430072 Wuhan, P.R. China cxxiao@whu.edu. cn H. Fu · C. -L. Tai Department of Computer Science & Engineering, Hong Kong University of Science & Technology, Hong Kong {fuhb, taicl}@cse.ust. hk Abstract This paper presents an efficient framework which supports both automatic and interactive shape extraction from surfaces. Unlike most of the existing hierarchical shape extraction methods, which are based on computationally expensive top-down algorithms, our framework employs a fast bottom-up hierarchical method with multiscale aggregation. We introduce a geometric similarity measure, which operates at multiple scales and guarantees that a hierarchy of high-level features are automatically found through local adaptive aggregation. We also show that the aggregation process allows easy incorporation of userspecified constraints, enabling users to interactively extract features of interest. Both our automatic and the interactive shape extraction methods do not require explicit connectivity information, and thus are applicable to unorganized point sets. Additionally, with the hierarchical feature representation, we design a simple and effective method to perform partial shape matching, allowing efficient search of self-similar features across the entire surface. Experiments show that our methods robustly extract visually meaningful features and are significantly faster than related methods.
BIOINFORMATICS APPLICATIONS

Summary: De novo assembly tools play a main role in reconstructing genomes from next-generation sequencing (NGS) data and usually yield a number of contigs. Using paired-read sequencing data it is possible to assess the order, distance and orientation of contigs and combine them into so-called scaffolds . Although the latter process is a crucial step in ﬁnishing genomes
Ireland GUIDING USE CASE AUTHORING : RESULTS OF AN EMPIRICAL STUDY by

This paper presents results from the first of two empirical studies which examine the effectiveness of guidelines for use case authoring. The ESPRIT 21.903 CREWS long-term research project has developed style and content guidelines for authoring use cases for requirements acquisition and validation. The effectiveness of these guidelines has been evaluated under different conditions. Results indicate that : i. the authoring guidelines improve the overall quality of the use case prose, ii the different guidelines work differently and with different levels of efficiency, and iii use cases are never entirely correctly written ; thus, they can be systematically corrected. The paper details a qualitative and quantitative comparison between guided and non-guided use case authoring. It outlines lessons learned and implications for the CREWS software tools design. 1. Use cases, scenarios and requirements There has been recently a considerable interest in use cases (UC) and scenarios for acquiring and validating system requirements. [1] recommends describing UCs using narrative text. However, little is known about the advantages and disadvantages of natural language UCs for acquiring requirements. The UML [2] proposes graphical notations such as sequence diagrams to describe UCs, but these notations do not permit expression needed in UCs for effective requirements acquisition and validation [3][4]. This paper evaluates different forms of guidance of natural language expression in scenario-based requirements engineering. There are clear advantages of narrative text for expressing UCs. Stakeholders are familiar with the notation, and do not need formal training or expensive software tools. However, narrative UCs are often ambiguous and lack structure. Figure 1 presents an example of UC describing distant purchase of goods. The main success scenario is a sequence of action descriptions. This sequence indicates a strict sequence of actions, and the main success scenario is separate from alternative course scenarios and variations [5]. MAIN SUCCESS SCENARIO 1. Buyer calls in with a purchase request. 2. Company captures buyer's name, address, requested goods, etc. 3. Company gives buyer information on goods, prices, delivery dates, etc. 4. Buyer signs for order. 5. Company creates order, ships order to buyer. 6. Company ships invoice to buyer. 7. Buyers pays invoice.
The GR2 Gripper: An Underactuated Hand for Open-Loop In-Hand Planar Manipulation

 Abstract —Performing dexterous manipulation of unknown objects with robot grippers without using high-fidelity contact sensors, active/sliding surfaces, or a priori workspace exploration is still an open problem in robot manipulation and a necessity for many robotics applications. In this paper, we present a two-fingered gripper topology that enables an enhanced predefined in-hand manipulation primitive controlled without knowing the size, shape, or other particularities of the grasped object. The in-hand manipulation behavior, namely, the planar manipulation of the grasped body, is predefined thanks to a simple hybrid low-level control scheme and has an increased range of motion due to the introduction of an elastic pivot joint between the two fingers. Experimental results with a prototype clearly show the advantages and benefits of the proposed concept. Given the generality of the topology and in-hand manipulation principle, researchers and designers working on multiple areas of robotics can benefit from the findings.
DeepDive

The dark data extraction or knowledge base construction (KBC) problem is to populate a relational database with information from unstructured data sources, such as emails, webpages, and PDFs. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help to develop KBC systems. The key idea in DeepDive is to frame traditional extract-transform-load (ETL) style data management problems as a single large statistical inference task that is declaratively defined by the user. DeepDive leverages the effectiveness and efficiency of statistical inference and machine learning for difficult extraction tasks, whereas not requiring users to directly write any probabilistic inference algorithms. Instead, domain experts interact with DeepDive by defining features or rules about the domain. DeepDive has been successfully applied to domains such as pharmacogenomics, paleobiology, and antihuman trafficking enforcement, achieving human-caliber quality at machine-caliber scale. We present the applications, abstractions, and techniques used in DeepDive to accelerate the construction of such dark data extraction systems.
Evaluating feature selection methods for learning in data mining applications

Recent advances in computing technology in terms of speed, cost, as well as access to tremendous amounts of computing power and the ability to process huge amounts of data in reasonable time have spurred increased interest in data mining applications. Machine learning has been one of the methods used in most of these data mining applications. The data used as input to any of these learning systems are the primary source of knowledge in terms of what is learned by these systems. There have been relatively few studies on preprocessing data used as input in these data mining systems. In this study, we evaluate several feature selection methods as to their effectiveness in preprocessing input data. We use real-world financial credit-risk data in evaluating these systems.
Small‐Scale Flux Transfer Events Formed in the Reconnection Exhaust Region Between Two X Lines

We report MMS observations of the ion‐scale flux transfer events (FTEs) that may involve two main X lines and tearing instability between the two X lines. The four spacecraft detected multiple isolated regions with enhanced magnetic field strength and bipolar Bn signatures normal to the nominal magnetopause, indicating FTEs. The currents within the FTEs flow mostly parallel to B, and the magnetic tension force is balanced by the total pressure gradient force. During these events, the plasma bulk flow velocity was directed southward. Detailed analysis of the magnetic and electric field and plasma moments variations suggests that the FTEs were initially embedded within the exhaust region north of an X line but were later located southward/downstream of a subsequent X line. The cross sections of the individual FTEs are in the range of ~2.5–6.8 ion inertial lengths. The observations suggest the formation of multiple secondary FTEs. The presence of an X line in the exhaust region southward of a second X line results from the southward drift of an old X line and the reformation of a new X line. The current layer between the two X lines is unstable to the tearing instability, generating multiple ion‐scale flux‐rope‐type secondary islands.
outputs myPlanet : an ontology driven Web based personalised news service

In this paperwe presentmyPlanet , an ontologydriven personalisedWeb-basedservice. We extended the existing infrastructureof the PlanetOnto newspublishingsystem.Our concernswere mainly to provide lightweight meansfor ontology maintenanceandeasetheaccessto repositoriesof newsitems,arich resourcefor informationsharing. We reasonaboutthe informationbeingsharedby providing anontology-driveninterest-profilingtool which enableusersto specify their interests. We also developedontology-driven heuristicsto find news itemsrelatedto users’interests. This paper arguesfor therole of ontology-drivenpersonalised Web-basedservicesin informationsharing.
Mixture of Experts Classi(cid:142)cation Using a Hierarchical Mixture Model

A three-level hierarchical mixture model for classi(cid:142)cation is presented that models the following data generation process : (1) the data are generated by a (cid:142)nite number of sources (clusters), and (2) the generation mechanism of each source assumes the existence of individual internal class-labeled sources (subclusters of the external cluster). The model estimates the posterior probability of class membership similar to a mixture of experts classi(cid:142)er. In order to learn the parameters of the model, we have developed a general training approach based on maximum likelihood that results in two ef(cid:142)cient training algorithms. Compared to other classi(cid:142)cation mixture models, the proposed hierarchical model exhibits several advantages and provides improved classi(cid:142)cation performance as indicated by the experimental results.
Resources and Tools for the Shallow Processing of Portuguese

This paper presents the linguistic resources and tools for the shallow processing of Portuguese developed in the scope of a research intitiative at the University of Lisbon. These resources include a 1 million token corpus that has been accurately hand annotated with a variety of linguistic information, as well as several state-of-the-art shallow processing tools capable of automatically producing that type of annotation. At present, the linguistic annotations in the corpus are sentence and paragraph boundaries, token boundaries, morphosyntactic POS categories, values of inflection features, lemmas and named-entities. Hence, the set of tools comprise a sentence chunker, a tokenizer, a POS tagger, nominal and verbal analyzers and lemmatizers, a verbal conjugator, a nominal “inflector”, and a named-entity recognizer, some of which underline several on-line services.
Earnings Management In Developed And Developing Countries: A Review Of Recent Literature

Over the years, opportunistic earnings management practice generates reliable accounting earnings in a smaller amount which could not produce the fair and true firms’ financial performance. Earnings management is most probably to decrease the quality in usefulness for investment decision and reported earnings, therefore, decreasing the confidence of investor on the financial reports. Though, accounting earnings have been more consistent with high quality while the manager’s opportunistic behavior is changed by applying monitoring system. Therefore, regulatory bodies of the stock market and other agencies are more concerned with the earnings management, particularly, after collapsing of current numerous firms to which were reported increasing the independence of external auditors and corporate governance. Its main objective is not only to directly expand corporate performance but also to resolve agency problems by making parallel management and shareholders’ interests. Earnings management, as for as the literature concerned is very rich and resolve the problems of earnings management such as type of earnings management, corporate governance practices and also numerous elements disturbing earning management. Past studies have also been recognized the constraints on earnings management and the impact of incentives which could be divided into two groups. The first group examines corporate governance, ownership structure and audit quality as constraints while the second group studies leverage and free cash flow as incentives. This study discusses the literature of earnings management and also discusses the suitable gap from the literature of earnings management in both developed and developing countries. The contribution of this study is to highlight the complication in issues of earnings management in reporting of financial corporate and gives a way for further study on decreasing earnings management practices.
Under Consideration for Publication in Knowledge and Information Systems Mining Sequential Patterns for Classification

While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper we describe BIDE-Discriminative, a modification of BIDE that uses class information for direct mining of predictive sequential patterns. We then perform an extensive evaluation on 9 real-life datasets of the different ways in which the basic BIDE-Discriminative can be used in real multi-class classification problems , including 1-vs-rest and model-based search tree approaches. The results of our experiments show that 1-vs-rest provides an efficient solution with good classification performance.
Grouping with Bias

We present a graph partitioning method to integrate prior knowledge in data grouping. We consider priors represented by three types of constraints: unitary constraints on labelling of groups, partial a priori grouping information, external in uence on binary constraints. They are modelled as biases in the grouping process. We incorporate these biases into graph partitioning criteria. Computationally this formulation leads to a constrained eigenproblem. We demonstrate the e ectiveness of this algorithm on image segmentation with priors and object detection with spatial attention.
Resistivity imaging in a fold and thrust belt using ZTEM and sparse MT data

As part of a CO2 reservoir exploration programme, NEOS acquired and analysed a variety of geophysical data within a ~2900 km2 region of the northern Raton Basin of southern Colorado (Figure 1a). The purpose of the survey was to better define the basin architecture and identify structures that may serve as traps for the CO2 gas. In addition to the Z-axis Tipper Electromagnetic (ZTEM) and magnetotelluric (MT) data discussed here, magnetic data were collected and publicly available ground gravity and satellite remote sensing data were analysed. We also had limited access to proprietary two-dimensional (2D) seismic and well data. Note that the acquisition area contains significant topography with elevations ranging from 1500 m above sea level in the basin to mountain peaks with elevations exceeding 4000 m.